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Yang SW, Xie Y, Liu JZ, Zhang D, Huang J, Liang P. A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT. Spectrochim Acta A Mol Biomol Spectrosc 2024; 317:124427. [PMID: 38754205 DOI: 10.1016/j.saa.2024.124427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/21/2024] [Accepted: 05/07/2024] [Indexed: 05/18/2024]
Abstract
The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.
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Affiliation(s)
- Si-Wei Yang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Yuhao Xie
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Jia-Zhen Liu
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - De Zhang
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Jie Huang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
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Alsharif ST, Almalki AH, Ramzy S, Sultan Alqahtani A, Abduljabbar MH, Algarni MA, Serag A. Derivative spectroscopy and wavelet transform as green spectrophotometric methods for abacavir and lamivudine measurement. Spectrochim Acta A Mol Biomol Spectrosc 2024; 310:123913. [PMID: 38271846 DOI: 10.1016/j.saa.2024.123913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
Herein, two different sustainable and green signal processing spectrophotometric approaches, namely, derivative spectroscopy and wavelet transform, have been utilized for effective measurement of the antiretroviral therapy abacavir and lamivudine in their pharmaceutical formulations. These methods were used to enhance the spectral data and differentiate between the absorption bands of abacavir and lamivudine in order to accurately measure their concentrations. For determining abacavir and lamivudine, the first derivative spectrophotometric method has been applied to the zero-order and ratio spectra of both drugs. The same approach has been tested using the continuous wavelet transform method where a second order 2.4 of rbio and bior wavelet families were found to be optimum for measuring both drugs. Validation of the proposed methods affirmed their reliability in terms of linearity over the concentration range 1.5-30 µg/mL and 1.5-36 µg/mL for abacavir and lamivudine, respectively, precision (RSD < 2 %), and accuracy with mean recoveries ranging between 98 % and 102 %. Additionally, these spectrophotometric methodologies were applied to real pharmaceutical preparations and yielded results congruent with a prior chromatographic method. Most prominently, the proposed methods stood out for their greenness and sustainability with 97 points as evaluated by the analytical eco-scale method and a score value of 0.79 as analyzed by AGREE method, thereby making them suitable for resource-limited settings and highlighting the potential for broader application of green analytical methods in pharmaceutical analysis.
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Affiliation(s)
- Shaker T Alsharif
- Department of Pharmaceutical Sciences, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Atiah H Almalki
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, 21944 Taif, Saudi Arabia; Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, P.O. Box 11099, 21944 Taif, Saudi Arabia
| | - Sherif Ramzy
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Arwa Sultan Alqahtani
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box, 90950, Riyadh 11623, Saudi Arabia
| | - Maram H Abduljabbar
- Department of Pharmacology and Toxicology, College of Pharmacy, Taif University, Taif 21944, Saudi Arabia
| | - Majed A Algarni
- Department of Clinical Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahmed Serag
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt.
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Qiu C, Li H, Qi C, Li B. Enhancing ECG classification with continuous wavelet transform and multi-branch transformer. Heliyon 2024; 10:e26147. [PMID: 38434292 PMCID: PMC10906304 DOI: 10.1016/j.heliyon.2024.e26147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 01/28/2024] [Accepted: 02/08/2024] [Indexed: 03/05/2024] Open
Abstract
Background Accurate classification of electrocardiogram (ECG) signals is crucial for automatic diagnosis of heart diseases. However, existing ECG classification methods often require complex preprocessing and denoising operations, and traditional convolutional neural network (CNN)-based methods struggle to capture complex relationships and high-level time-series features. Method In this study, we propose an ECG classification method based on continuous wavelet transform and multi-branch transformer. The method utilizes continuous wavelet transform (CWT) to convert the ECG signal into time-series feature map, eliminating the need for complicated preprocessing. Additionally, the multi-branch transformer is introduced to enhance feature extraction during model training and improve classification performance by removing redundant information while preserving important features. Results The proposed method was evaluated on the CPSC 2018 (6877 cases) and MIT-BIH (47 cases) ECG public datasets, achieving an accuracy of 98.53% and 99.38%, respectively, with F1 scores of 97.57% and 98.65%. These results outperformed most existing methods, demonstrating the excellent performance of the proposed method. Conclusion The proposed method accurately classifies the ECG time-series feature map, which holds promise for the diagnosis of cardiac arrhythmias. The findings of this study are valuable for advancing the field of automatic ECG diagnosis.
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Affiliation(s)
- Chenyang Qiu
- School of Information Technology, Yunnan University, Kunming, China
| | - Hao Li
- School of Information Technology, Yunnan University, Kunming, China
| | - Chaoqun Qi
- School of Information Technology, Yunnan University, Kunming, China
| | - Bo Li
- School of Information Technology, Yunnan University, Kunming, China
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Priyadarshini MS, Bajaj M, Prokop L, Berhanu M. Perception of power quality disturbances using Fourier, Short-Time Fourier, continuous and discrete wavelet transforms. Sci Rep 2024; 14:3443. [PMID: 38341467 DOI: 10.1038/s41598-024-53792-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time-frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.
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Affiliation(s)
- M S Priyadarshini
- Department of Electrical and Electronics Engineering, K. S. R. M. College of Engineering (Autonomous), Kadapa, 516005, India
| | - Mohit Bajaj
- Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India.
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
- Graphic Era Hill University, Dehradun, 248002, India.
- Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan.
| | - Lukas Prokop
- ENET Centre, VSB-Technical University of Ostrava, 708 00, Ostrava, Czech Republic
| | - Milkias Berhanu
- Department of Electrical and Computer Engineering, College of Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
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Abedinzadeh Torghabeh F, Modaresnia Y, Moattar MH. Hybrid deep transfer learning-based early diagnosis of autism spectrum disorder using scalogram representation of electroencephalography signals. Med Biol Eng Comput 2024; 62:495-503. [PMID: 37938451 DOI: 10.1007/s11517-023-02959-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023]
Abstract
Early diagnosis of autism spectrum disorder (ASD) plays an important role in the rehabilitation of the patient. This goal necessitates higher-level pattern representation and a strong modeling approach. The proposed approach applies scalogram images of electroencephalography signals for the first purpose and a two-level deep learning architecture for better classification. Scalogram images embed both the temporal and spectral information of the signal. On the other hand, the hybrid deep learning hierarchy of convolutional neural network followed by long short-term memory models both spatial and temporal information of the scalogram image. The approach is evaluated on a dataset of 34 ASD samples and 11 normal cases in without-voice and with-voice conditions. To validate the early diagnosis hypothesis, signals from children older than 5 years are used as the training set, and signals from younger subjects are used as the validation set. The proposed method achieves excellent performance of 99.50% and 98.43% for automatically detecting ASD with and without voice, respectively. This classification performance is higher than most recent reported approaches, and the results show the effectiveness of the approach in early diagnosis of ASD and demonstrate the auditory impact on the diagnosis of autism.
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Affiliation(s)
| | - Yeganeh Modaresnia
- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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Prigent G, Aminian K, Cereatti A, Salis F, Bonci T, Scott K, Mazzà C, Alcock L, Del Din S, Gazit E, Hansen C, Paraschiv-Ionescu A. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings. Med Biol Eng Comput 2023; 61:2341-2352. [PMID: 37069465 PMCID: PMC10412496 DOI: 10.1007/s11517-023-02826-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/08/2023] [Indexed: 04/19/2023]
Abstract
Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.
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Affiliation(s)
- Gaëlle Prigent
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - for the Mobilise-D consortium
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
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Roy AS, Freed JH, Srivastava M. Differentiating Unimodal and Multimodal Distributions in Pulsed Dipolar Spectroscopy Using Wavelet Transforms. Res Sq 2023:rs.3.rs-3216615. [PMID: 37577617 PMCID: PMC10418556 DOI: 10.21203/rs.3.rs-3216615/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Site directed spin labeling has enabled protein structure determination using electron spin resonance (ESR) pulsed dipolar spectroscopy (PDS). Small details in a distance distribution can be key to understanding important protein structure-function relationships. A major challenge has been to differentiate unimodal and overlapped multimodal distance distributions. They often yield similar distributions and dipolar signals. Current model-free distance reconstruction techniques such as Srivastava-Freed Singular Value Decomposition (SF-SVD) and Tikhonov regularization can suppress these small features in uncertainty and/or error bounds, despite being present. In this work, we demonstrate that continuous wavelet transform (CWT) can distinguish PDS signals from unimodal and multimodal distance distributions. We show that periodicity in CWT representation reflects unimodal distributions, which is masked for multimodal cases. This work is meant as a precursor to a cross-validation technique, which could indicate the modality of the distance distribution.
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Affiliation(s)
- Aritro Sinha Roy
- Department of Chemistry and Chemical Biology, Cornell University, Baker Laboratory, Ithaca, 14853, NY, USA
- National Biomedical Resource for Advanced ESR Spectroscopy, Cornell University, Baker Laboratory, Ithaca, 14853, NY, USA
| | - Jack H. Freed
- Department of Chemistry and Chemical Biology, Cornell University, Baker Laboratory, Ithaca, 14853, NY, USA
- National Biomedical Resource for Advanced ESR Spectroscopy, Cornell University, Baker Laboratory, Ithaca, 14853, NY, USA
| | - Madhur Srivastava
- Department of Chemistry and Chemical Biology, Cornell University, Baker Laboratory, Ithaca, 14853, NY, USA
- National Biomedical Resource for Advanced ESR Spectroscopy, Cornell University, Baker Laboratory, Ithaca, 14853, NY, USA
- Cornell Atkinson Center for Sustainability, Cornell University, 340 Tower Road, Ithaca, 14853, NY, USA
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田 蕴, 周 强, 李 婉. [Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:286-294. [PMID: 37139760 PMCID: PMC10162915 DOI: 10.7507/1001-5515.202211021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/27/2023] [Indexed: 05/05/2023]
Abstract
The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.
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Affiliation(s)
- 蕴郅 田
- 陕西科技大学 电气与控制工程学院(西安 710021)School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China
- 陕西科技大学 电子信息与人工智能学院(西安 710021)School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China
| | - 强 周
- 陕西科技大学 电气与控制工程学院(西安 710021)School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China
- 陕西科技大学 电子信息与人工智能学院(西安 710021)School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China
| | - 婉 李
- 陕西科技大学 电气与控制工程学院(西安 710021)School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China
- 陕西科技大学 电子信息与人工智能学院(西安 710021)School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China
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9
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Kavuran G, Gökhan Ş, Yeroğlu C. COVID-19 and human development: An approach for classification of HDI with deep CNN. Biomed Signal Process Control 2023; 81:104499. [PMID: 36530217 DOI: 10.1016/j.bspc.2022.104499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/18/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
The measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Convolutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).
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Jiménez-González A, Salas-Márquez U. Time-frequency characteristics of the vibrations underlying the first fetal heart sound: a preliminary study. Med Biol Eng Comput 2023; 61:739-756. [PMID: 36598675 DOI: 10.1007/s11517-022-02756-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/22/2022] [Indexed: 01/05/2023]
Abstract
This work studied, for the first time, the time-frequency characteristics of the vibrations underlying the first fetal heart sound (S1). To this end, the continuous wavelet transform was used to produce time-energy and time-frequency representations of S1 from where five vibrations were studied by their timing, energy, and frequency characteristics in three gestational age groups (early, G1, preterm, G2, and term, G3). Results on a dataset of 1111 S1s (9 phonocardiograms between 33 and 40 weeks) indicate that such representations uncovered a set of five well-defined, non-overlapped, and large-energy vibrations whose features presented interesting behaviors. Thus, for each group, while the timing characteristics of the five vibrations were likely to be statically different, their frequencies were similar. Also, the energies of the vibrations were likely to be different only in G2 and G3. Alternatively, while the frequencies and energies of each vibration were likely to statistically change among groups (excluding the energy of the third vibration), the timings were more likely to change only from G1 to G2 and from G2 to G3. Therefore, this methodology seems suitable to detect and study the generating vibrations of S1. Future work will test the correlation between these vibrations and the valvular events.
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Affiliation(s)
- Aída Jiménez-González
- Department of Electrical Engineering, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, Col. Vicentina, Alcaldía Iztapalapa, C.P. 09340, México City, México.
| | - Usiel Salas-Márquez
- Department of Electrical Engineering, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, Col. Vicentina, Alcaldía Iztapalapa, C.P. 09340, México City, México
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Wu M, Hu X, Wang Z, Zeng X. Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China. Ecol Indic 2023; 146:109862. [PMID: 36624881 PMCID: PMC9812845 DOI: 10.1016/j.ecolind.2023.109862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O3, the annual average concentration of NO2, PM2.5 and SO2 showed a decreasing trend during 2016 and 2019; NO2, PM2.5 and SO2 show a trend of "low in summer and high in winter"; the diurnal variation of NO2 concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO2 and NO2 had four primary periods, while O3 only had two primary periods. The high NO2 concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO2 and SO2 decreased considerably during lockdown, whereas PM2.5 and O3 increased dramatically. The contribution rates of transportation activities to the NO2, O3, PM2.5 and SO2 were estimated be 9.4 % ∼ 17.2 %, -76.4 % ∼ -42.9 %, -39.5 % ∼ -22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and -10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.
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Affiliation(s)
- Min Wu
- Department of Transportation Engineering, Fujian Forestry Vocational Technical College, Nanping 353000, China
- College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xisheng Hu
- College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zhanyong Wang
- College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xiaoying Zeng
- Department of Rail Transit, Fujian Chuanzheng Communications College, Fuzhou 350007, China
- College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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12
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Sarkar A, Hossain SKS, Sarkar R. Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm. Neural Comput Appl 2023; 35:5165-5191. [PMID: 36311167 PMCID: PMC9596348 DOI: 10.1007/s00521-022-07911-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 09/29/2022] [Indexed: 12/01/2022]
Abstract
Capturing time and frequency relationships of time series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use Continuous Wavelet Transform to encode the time series of sensor data as multi-channel images. Then, we utilize a Spatial Attention-aided Convolutional Neural Network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: Mutual Information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the Genetic Algorithm (GA). The K-Nearest Neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here https://github.com/apusarkar2195/HAR_WaveletTransform_SpatialAttention_FeatureSelection.
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Affiliation(s)
- Apu Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - S. K. Sabbir Hossain
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. A Hybrid EEG-based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks and Support Vector Machine. Basic Clin Neurosci 2023; 14:87-102. [PMID: 37346875 PMCID: PMC10279985 DOI: 10.32598/bcn.2021.3133.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/06/2021] [Accepted: 04/14/2021] [Indexed: 11/02/2023] Open
Abstract
Introduction Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. Methods In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Conclusion Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.
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Affiliation(s)
- Sara Bagherzadeh
- Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran
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14
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Zhou J, Li J, Gao W, Zhang S, Wang C, Lin J, Zhang S, Yu J, Tang K. Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection. Biochem Biophys Res Commun 2022; 624:75-80. [PMID: 35940130 DOI: 10.1016/j.bbrc.2022.07.083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/28/2022]
Abstract
Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.
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Affiliation(s)
- Junfei Zhou
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, PR China; Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, PR China
| | - Junhui Li
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, PR China; Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, PR China
| | - Wenqing Gao
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, PR China.
| | - Shun Zhang
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, PR China; Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, 2019E10020, Ningbo, PR China
| | - Chenlu Wang
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, PR China
| | - Jing Lin
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, PR China; Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, 2019E10020, Ningbo, PR China
| | - Sijia Zhang
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, PR China; Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, 2019E10020, Ningbo, PR China
| | - Jiancheng Yu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, PR China; Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, PR China.
| | - Keqi Tang
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, PR China.
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15
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Sabherwal P, Agrawal M, Singh L. Independent Detection of T-Waves in Single Lead ECG Signal Using Continuous Wavelet Transform. Cardiovasc Eng Technol 2022. [PMID: 36163602 DOI: 10.1007/s13239-022-00643-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 09/02/2022] [Indexed: 11/02/2022]
Abstract
INTRODUCTION In the ECG signals, T-waves play a very important role in the detection of cardiac arrest. During myocardial ischemia, the first significant change occurs on the T-wave. These waves are generated due to the repolarization of the heart ventricle. The independent detection of T-waves is a bit challenging due to its variable nature, therefore, most of the algorithms available in the literature for T-wave detection use the detection of the QRS complex as the starting point. But accurate detection of Twave is very much required, as clinically, the first indication of a shortage of blood supply to the heart muscle (myocardial ischemia) shows up as changes in T-wave followed by other changes in the morphology of the ECG signal. MATERIALS AND METHODS In this paper, an efficient and novel algorithm based on Continuous Wavelet Transform (CWT) is presented to detect the Twave independently. In CWT, for better matching, a new mother wavelet is designed using the pattern and shape of the Twave. This algorithm is validated on all the signals of the QT database. CONCLUSION The algorithm attains an average sensitivity of 99.88% and positive predictivity of 99.81% for the signals annotated by the cardiologists in the database.
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Thirukkumaran K, Mukhopadhyay CK. Acoustic emission signals analysis to differentiate the damage mechanism in the drilling of Al-5%B 4C metal matrix composite. Ultrasonics 2022; 124:106762. [PMID: 35644099 DOI: 10.1016/j.ultras.2022.106762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/20/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
Tool wear leads to dimensional inaccuracy and low surface quality in the workpiece, and unexpected sudden tool failure. Detection of tool wear is essential to enhance the quality of manufacturing components and extend tool life. The present work is aimed to investigate the various damage mechanisms involved in the cutting tool and workpiece during drilling of Al-5%B4C composite using acoustic emission technique (AET). The dry drilling experiments were carried out at different spindle speeds and feed rates with high strength steel (HSS) tool. AE time-domain parameters such as count, energy, amplitude and root mean square (RMS) voltage were extracted from the signals and correlated with cutting parameters and tool damage. Fast Fourier transform (FFT) was applied to visualize the frequency components in the AE signals during the drilling process. The wavelet packet transform (WPT) approach was performed to the AE signals to identify and discriminate the various damage mechanism involved in the drilling. The differentiated damage mechanism and their corresponding wavelet energy content were studied. The wavelet energy ratio for decomposed components at different speeds was discussed. The vision measuring microscope was employed to measure the tool wear. The AE features, i.e., AERMS and wavelet coefficient increases with increasing tool wear. A scanning electron microscope was also utilized to characterize the microstructural damage present in the cutting tool and workpiece.
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Affiliation(s)
- K Thirukkumaran
- Non-destructive Evaluation Division, Metallurgy and Materials Group, IGCAR, India; Homi Bhabha National Institute, Kalpakkam, Tamil Nadu 603102, India
| | - C K Mukhopadhyay
- Homi Bhabha National Institute, Kalpakkam, Tamil Nadu 603102, India.
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17
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Ben Othman D, Abida H. Monitoring and mapping of drought in a semi-arid region: case of the Merguellil watershed, central Tunisia. Environ Monit Assess 2022; 194:287. [PMID: 35305173 DOI: 10.1007/s10661-022-09926-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 03/05/2022] [Indexed: 06/14/2023]
Abstract
Drought is defined as a period of time characterized by below-normal water availability, which may affect crops, animals and the environment. Recently, drought was shown to be more frequent and more intense, implying thereby the need for monitoring and analysis of this natural hazard. The present study aims to examine the spatial extent and temporal variation of droughts in the Merguellil watershed, located in central Tunisia. This contribution was mainly based on the analysis of annual and monthly rainfall time series recorded over the period (1983-2018) in 19 stations spread throughout the study watershed. Rainfall trend was first examined using the Mann-Kendall statistical test. Then, statistical (standard precipitation index (SPI) and Palmer drought severity index (PDSI)), spectral (continuous wavelet transform (CWT)) and mapping (geographical information system (GIS)) techniques were used to identify extreme dry events and to characterize their severity and their spatial and temporal extents. The results obtained revealed the recurrence and frequency of drought conditions in the Merguellil watershed over the study period. Seven drought sequences (1983-1984, 1986-1989, 1992-1995, 1999-2002, 2007-2009, 2013-2015 and 2017-2018), with different levels of severity, were distinguished based on the computed SPI and PDSI values. Spectral analysis of rainfall data also showed the occurrence of significant droughts in recent years. The period starting from 2010 was shown to be marked by recurrent episodes of drought in the Merguellil watershed. Extreme drought events mapping over this period confirmed drought severity at both time and space scales. All of these findings may be helpful for developing programs of water resource management in the study watershed.
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Affiliation(s)
- Dhouha Ben Othman
- Laboratory of Modeling of Geological and Hydrological Systems (GEOMODELE), Faculty of Sciences, University of Sfax, 3000, Sfax, Tunisia.
| | - Habib Abida
- Laboratory of Modeling of Geological and Hydrological Systems (GEOMODELE), Faculty of Sciences, University of Sfax, 3000, Sfax, Tunisia
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18
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Valizadeh M, Sohrabi M, Ameri Braki Z, Rashidi R, Pezeshkpur M. Investigation of spectrophotometric simultaneous absorption of Salmeterol and Fluticasone in Seroflo spray by continuous wavelet transform and radial basis function neural network methods. Spectrochim Acta A Mol Biomol Spectrosc 2021; 263:120192. [PMID: 34314967 DOI: 10.1016/j.saa.2021.120192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/06/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
In this research, the simultaneous absorption of Salmeterol (SAL) and Fluticasone (FLU) in Seroflo spray was investigated using a spectrophotometric device via employing continuous wavelet transform (CWT) and radial basis function neural network (RBF-NN) methods. Root mean square error (RMSE) related to the RBF model was obtained 3.17 × 10-13 and 1.41 × 10-13 for SAL and FLU, respectively. Limit of detection (LOD) and limit of quantification (LOQ) corresponding to the CWT method were 0.004, 0.280 μg/mL, and 0.431, 0.479 μg/mL for SAL and FLU, respectively. Root mean square error (RMSE) of SAL and FLU was obtained 3.17 × 10-13 and 1.41 × 10-13, respectively in RBF-NN method. In the end, the results obtained from all methods were compared with the high-performance liquid chromatography (HPLC) as a reference method. According to the one-way analysis of variance with a 95% confidence level, there is no significant difference between the proposed techniques and HPLC. Therefore, chemometrics methods are sufficiently accurate, as the reference method for the analysis of drugs. The suggested methods are simple, fast, and cheap. Also, there is no need for pre-preparation steps. These methods can be used for quality control laboratories in the pharmaceutical industry.
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Affiliation(s)
- Maryam Valizadeh
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Melika Sohrabi
- Faculty of Veterinary Medicine, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Zahra Ameri Braki
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Rashed Rashidi
- Faculty of Civil, Water and Environmental engineering, Shahid Beheshti University of Iran, Tehran, Iran
| | - Maryam Pezeshkpur
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
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López-Dorado A, Pérez J, Rodrigo M, Miguel-Jiménez J, Ortiz M, de Santiago L, López-Guillén E, Blanco R, Cavalliere C, Morla EMS, Boquete L, Garcia-Martin E. Diagnosis of multiple sclerosis using multifocal ERG data feature fusion. Inf Fusion 2021; 76:157-167. [PMID: 34867127 PMCID: PMC8475498 DOI: 10.1016/j.inffus.2021.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 11/15/2020] [Accepted: 05/17/2021] [Indexed: 05/16/2023]
Abstract
The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.
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Affiliation(s)
- A. López-Dorado
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - J. Pérez
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
| | - M.J. Rodrigo
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - J.M. Miguel-Jiménez
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - M. Ortiz
- School of Physics, University of Melbourne, VIC 3010, Australia
| | - L. de Santiago
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - E. López-Guillén
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - R. Blanco
- Department of Surgery, Medical and Social Sciences, University of Alcalá, Alcalá de Henares, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - C. Cavalliere
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - E. Mª Sánchez Morla
- Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), 28041 Madrid, Spain
- Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
- CIBERSAM: Biomedical Research Networking Centre in Mental Health, 28029 Madrid, Spain
| | - L. Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - E. Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
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20
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Zhao R, An L, Song D, Li M, Qiao L, Liu N, Sun H. Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis. Spectrochim Acta A Mol Biomol Spectrosc 2021; 259:119768. [PMID: 33971438 DOI: 10.1016/j.saa.2021.119768] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/21/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.
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Affiliation(s)
- Ruomei Zhao
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Lulu An
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Di Song
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Minzan Li
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affffairs, China Agricultural University, Beijing 100083, China
| | - Lang Qiao
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Ning Liu
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affffairs, China Agricultural University, Beijing 100083, China
| | - Hong Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
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Singh SA, Meitei TG, Devi ND, Majumder S. A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images. Phys Eng Sci Med 2021; 44:1221-1230. [PMID: 34550551 DOI: 10.1007/s13246-021-01057-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/03/2021] [Indexed: 10/20/2022]
Abstract
Brain-computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.
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22
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Sabbaghi H, Behbahani S, Daftarian N, Ahmadieh H. New criteria for evaluation of electroretinogram in patients with retinitis pigmentosa. Doc Ophthalmol 2021; 143:271-281. [PMID: 34191198 DOI: 10.1007/s10633-021-09843-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 05/21/2021] [Indexed: 12/01/2022]
Abstract
BACKGROUND Electroretinogram (ERG) plays an essential role in the diagnosis of retinal disease. Choosing appropriate methods could extract valuable information from ERG. In this study, a new criterion based on time-frequency domain analysis was proposed to investigate the retina in retinitis pigmentosa (RP) patients. MATERIALS AND METHODS The total number of 16 eyes from eight RP patients and 20 eyes from age-matched healthy subjects were assessed. The signals included photopic and scotopic ERGs. Continuous wavelet transform was applied to ERGs. Dominant frequencies were extracted, and the contours related to these dominant frequencies were selected. As a new criterion, the areas related to dominant frequency contours were considered a feature to differentiate the RP and normal groups. To better evaluate the proposed criterion results, the time-domain analysis characteristics of ERG were also considered. RESULTS The results showed an increase in implicit time and reduced amplitude in RP patients (P < 0.05). A significant decrease of dominant frequencies and increasing their occurrence time were seen in ERG of RP patients. Also, in RP patients, the third dominant frequency was disappeared from the three main frequencies observed in photopic ERGs of normal subjects. The area criterion showed a significant decrease in RP groups (P < 0.05). CONCLUSION RP can cause changes in the time and time-frequency components of the ERG. The area index could represent a new view of the characteristics of the ERG in the time-frequency domain. This criterion can help the ophthalmologist to have a better evaluation of retinal disease.
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Affiliation(s)
- Hamideh Sabbaghi
- Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroor Behbahani
- Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Narsis Daftarian
- Ocular Tissue Engineering Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Ahmadieh
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Mashrur FR, Islam MS, Saha DK, Islam SMR, Moni MA. SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals. Comput Biol Med 2021; 134:104532. [PMID: 34102402 DOI: 10.1016/j.compbiomed.2021.104532] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 11/19/2022]
Abstract
Sleep apnea is a common symptomatic disease affecting nearly 1 billion people around the world. The gold standard approach for determining the severity of sleep apnea is full-night polysomnography conducted in the laboratory, which is very costly and cumbersome. In this work, we propose a novel scalogram-based convolutional neural network (SCNN) to detect obstructive sleep apnea (OSA) using single-lead electrocardiogram (ECG) signals. Firstly, we use continuous wavelet transform (CWT) to convert ECG signals into conventional scalograms. In parallel, we also apply empirical mode decomposition (EMD) to the signals to find correlated intrinsic mode functions (IMFs) and then apply CWT on the IMFs to obtain hybrid scalograms. Finally, we train a lightweight CNN model on these scalograms to extract deep features for OSA detection. Experiments on the benchmark Apnea-ECG dataset demonstrate that our proposed model results in an accuracy of 94.30%, sensitivity 94.30%, specificity 94.51%, and F1-score 95.85% in per-segment classification. Our model also achieves an accuracy of 81.86%, sensitivity 71.62%, specificity 86.05%, and F1-score 69.63% for UCDDB dataset. Furthermore, our model achieves an accuracy of 100.00% in per-recording classification for Apnea-ECG dataset. The experimental results outperform the existing OSA detection approaches using ECG signals.
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Affiliation(s)
- Fazla Rabbi Mashrur
- Department of Biomedical Engineering, Khulna University of Engineering & Technology, Bangladesh.
| | - Md Saiful Islam
- School of Information and Communication Technology, Griffith University, Gold Coast, Australia.
| | - Dabasish Kumar Saha
- Department of Biomedical Engineering, Khulna University of Engineering & Technology, Bangladesh.
| | - S M Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, South Korea.
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, University of New South Wales, Australia.
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Keyvan K, Sohrabi MR, Motiee F. Improved spectral resolution for the rapid simultaneous spectrophotometric determination of sofosbuvir and daclatasvir as anti hepatitis C virus drugs in pharmaceutical formulation and biological fluid using continuous wavelet and derivative transform. Spectrochim Acta A Mol Biomol Spectrosc 2021; 251:119429. [PMID: 33477087 DOI: 10.1016/j.saa.2021.119429] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/22/2020] [Accepted: 12/31/2020] [Indexed: 06/12/2023]
Abstract
In this study, the simultaneous spectrophotometric estimation of Sofosbuvir (SOF) and Daclatasvir (DAC) in synthetic mixtures and tablet formulation in the presence of overlapping spectra was performed based on continuous wavelet transform (CWT) and derivative spectrophotometry (DS) methods without any separation process. The Coiflet (Coif2) and Daubechies (Db3) wavelet families with wavelength of 256 nm and 218 nm were obtained as the best families for the simultaneous determination of SOF and DAC, respectively. Also, the first derivative absorption spectra revealed the best results corresponding to the analysis of SOF and DAC at 237 nm and 291 nm, respectively. The ranges of limit of detection (LOD) and limit of quantitation (LOQ) related to the CWT and DS methods were 2.45 × 10-3 to 0.5054 and 6.91 × 10-3 to 0.6027, respectively. Mean recovery values of SOF and DAC in synthetic mixtures for CWT approach were 98.55%, 98.09% and in DS method were 98.78% and 95.83%, respectively. Real samples, including Sovodak tablet and urine was used for accurate simultaneous determination of the mentioned components. Analyzing Sovodak tablet was implemented using high-performance liquid chromatography (HPLC) as a reference method that the results were near to the CWT and DS methods. In order to investigate the existence of significant differences between the methods, analysis of variance (ANOVA) test at the 95% confidence level was performed but no significant differences were observed. In addition, the amounts of SOF and DAC in the complex matrix of biological sample were well predicted by the proposed methods.
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Affiliation(s)
- Kiarash Keyvan
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahmoud Reza Sohrabi
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Fereshteh Motiee
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
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25
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Mahendra Kumar JL, Rashid M, Muazu Musa R, Mohd Razman MA, Sulaiman N, Jailani R, P.P. Abdul Majeed A. The classification of EEG-based winking signals: a transfer learning and random forest pipeline. PeerJ 2021; 9:e11182. [PMID: 33850667 PMCID: PMC8019310 DOI: 10.7717/peerj.11182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/08/2021] [Indexed: 11/20/2022] Open
Abstract
Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.
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Affiliation(s)
- Jothi Letchumy Mahendra Kumar
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia
| | - Mamunur Rashid
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Liberal Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Mohd Azraai Mohd Razman
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia
| | - Norizam Sulaiman
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rozita Jailani
- Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Gambang, Malaysia
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Hu W, Yao J, He Q, Chen J. Changes in precipitation amounts and extremes across Xinjiang (northwest China) and their connection to climate indices. PeerJ 2021; 9:e10792. [PMID: 33552744 PMCID: PMC7842144 DOI: 10.7717/peerj.10792] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 12/28/2020] [Indexed: 12/03/2022] Open
Abstract
Xinjiang is a major part of China’s arid region and its water resource is extremely scarcity. The change in precipitation amounts and extremes is of significant importance for the reliable management of regional water resources in this region. Thus, this study explored the spatiotemporal changes in extreme precipitation using the Mann–Kendall (M–K) trend analysis, mutation test, and probability distribution functions, based on the observed daily precipitation data from 89 weather stations in Xinjiang, China during 1961–2018. We also examined the correlations between extreme precipitation and climate indices using the cross-wavelet analysis. The results indicated that the climate in Xinjiang is becoming wetter and the intensity and frequency of extreme precipitation has begun to strengthen, with these trends being more obvious after the 1990s. Extreme precipitation trends displayed spatial heterogeneity in Xinjiang. Extreme precipitation was mainly concentrated in mountainous areas, northern Xinjiang, and western Xinjiang. The significant increasing trend of extreme precipitation was also concentrated in the Tianshan Mountains and in northern Xinjiang. In addition, the climate indices, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Multivariate ENSO Index and Indian Ocean Dipole Index had obvious relationships with extreme precipitation in Xinjiang. The relationships between the extreme precipitation and climate indices were not clearly positive or negative, with many correlations advanced or delayed in phase. At the same time, extreme precipitation displayed periodic changes, with a frequency of approximately 1–3 or 4–7 years. These periodic changes were more obvious after the 1990s; however, the exact mechanisms involved in this require further study.
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Affiliation(s)
- Wenfeng Hu
- Fuyang Normal University, History, Culture and Tourism School, Fuyang, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
| | - Junqiang Yao
- Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
| | - Qing He
- Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
| | - Jing Chen
- Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
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Lostanlen V, El-Hajj C, Rossignol M, Lafay G, Andén J, Lagrange M. Time-frequency scattering accurately models auditory similarities between instrumental playing techniques. EURASIP J Audio Speech Music Process 2021; 2021:3. [PMID: 33488686 PMCID: PMC7801324 DOI: 10.1186/s13636-020-00187-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 11/09/2020] [Indexed: 06/12/2023]
Abstract
Instrumentalplaying techniques such as vibratos, glissandos, and trills often denote musical expressivity, both in classical and folk contexts. However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called "ordinary" technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject. In this article, we ask 31 human participants to organize 78 isolated notes into a set of timbre clusters. Analyzing their responses suggests that timbre perception operates within a more flexible taxonomy than those provided by instruments or playing techniques alone. In addition, we propose a machine listening model to recover the cluster graph of auditory similarities across instruments, mutes, and techniques. Our model relies on joint time-frequency scattering features to extract spectrotemporal modulations as acoustic features. Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we report a state-of-the-art average precision at rank five (AP@5) of 99.0%±1. An ablation study demonstrates that removing either the joint time-frequency scattering transform or the metric learning algorithm noticeably degrades performance.
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Affiliation(s)
- Vincent Lostanlen
- LS2N, CNRS, Centrale Nantes, Nantes University, 1, rue de la Noe, Nantes, 44000 France
| | - Christian El-Hajj
- LS2N, CNRS, Centrale Nantes, Nantes University, 1, rue de la Noe, Nantes, 44000 France
| | | | | | - Joakim Andén
- Department of Mathematics, KTH Royal Institute of Technology, Lindstedtsvägen 25, Stockholm, SE-100 44 Sweden
- Center for Computational Mathematics, Flatiron Institute, 162 5th Avenue, New York, 10010 NY USA
| | - Mathieu Lagrange
- LS2N, CNRS, Centrale Nantes, Nantes University, 1, rue de la Noe, Nantes, 44000 France
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Valizadeh M, Sohrabi MR, Motiee F. The application of continuous wavelet transform based on spectrophotometric method and high-performance liquid chromatography for simultaneous determination of anti-glaucoma drugs in eye drop. Spectrochim Acta A Mol Biomol Spectrosc 2020; 242:118777. [PMID: 32801022 DOI: 10.1016/j.saa.2020.118777] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/22/2020] [Accepted: 07/29/2020] [Indexed: 06/11/2023]
Abstract
In this study, a fast, low-cost, accurate, and precise spectrophotometric method based on the continuous wavelet transform (CWT) was assayed to determine dorzolamide (DOR) and timolol (TIM) in an eye drop sample simultaneously. Different wavelet families were investigated to select the best family for analyzing the DOR and TIM. The Mexican hat wavelet (MHW) family with the wavelength of 281 nm and Gaussian wavelet family (gaus2) in the wavelength of 267 nm were found for the simultaneous analysis of DOR and TIM, respectively. Mean recovery values of synthetic mixtures were found 97.44%±2.63 and 99.18%±4.00 for DOR and TIM, respectively. The root mean square errors (RMSE) of DOR and TIM were achieved 0.5550 and 0.3306, respectively. Eye drop as a real sample was analyzed by spectrophotometry coupled with the CWT technique, as well as high-performance liquid chromatography (HPLC) as a reference method. The obtained results were compared with each other by the one-way analysis of variance (ANOVA) test and there was no significant difference between them.
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Affiliation(s)
- Maryam Valizadeh
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahmoud Reza Sohrabi
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Fereshte Motiee
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
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29
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Ahmadieh H, Behbahani S, Safi S. Continuous wavelet transform analysis of ERG in patients with diabetic retinopathy. Doc Ophthalmol 2020; 142:305-314. [PMID: 33226538 DOI: 10.1007/s10633-020-09805-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/10/2020] [Indexed: 01/02/2023]
Abstract
PURPOSE Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. Non-proliferative diabetic retinopathy (NPDR) is a stage of the disease that contains morphological and functional disruption of the retinal vasculature and dysfunction of retinal neurons. This study aimed to compare time and time-frequency-domain analysis in the evaluation of electroretinograms (ERGs) in subjects with NPDR. METHOD The ERG responses were recorded in 16 eyes from 12 patients with NPDR and 24 eyes from 12 healthy subjects as the control group. The implicit time, amplitude, and time-frequency-domain parameters of photopic and scotopic ERGs were analyzed. RESULTS The implicit times of b-waves in the dark-adapted 10.0 (P = 0.0513) and light-adapted 3.0 (P = 0.0414) were significantly increased in the NPDR group. The amplitudes of a- and b-wave showed a significantly decreased dark-adapted 10.0 (P = 0.0212; P = 0.0133) and light-adapted 3.0 (P = 0.0517; P = 0.0021) ERG of the NPDR group. The Cohen's d effect size had higher values in the amplitude of dark-adapted 10.0 b-wave (|d|= 1.8058) and amplitude of light-adapted 3.0 b-wave (|d|= 1.9662). The CWT results showed that the frequency ranges of the dominant components in dark-adapted 10.0 and light-adapted 3.0 ERG were decreased in the NPDR group compared to the healthy group (P < 0.05). The times associated with the NDPR group's dominant components were increased compared to normal eyes in both dark-adapted 10.0 and light-adapted 3.0 ERG (P < 0.05). All Cohen's d effect sizes of the implicit times and dominant frequency components were on a large scale (|d|> 1). CONCLUSION These findings suggest that the time and time-frequency parameters of both photopic and scotopic ERGs can be good indicators for DR. However, time-frequency-domain analysis could present more information might be helpful in the assessment of the DR severity.
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Affiliation(s)
- Hamid Ahmadieh
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroor Behbahani
- Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Sare Safi
- Ophthalmic Epidemiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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30
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Sun Y, Cui X, Cai W, Shao X. Understanding the complexity of the structures in alcohol solutions by temperature-dependent near-infrared spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 2020; 229:117864. [PMID: 31806476 DOI: 10.1016/j.saa.2019.117864] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 11/05/2019] [Accepted: 11/25/2019] [Indexed: 05/13/2023]
Abstract
For understanding the structures and the hydrogen bonding in alcohol solutions, the changes of the structures and hydrogen bonding with temperature were studied by temperature-dependent near-infrared (NIR) spectroscopy. The spectral features of eight alcohol species including the monomer, dimer and linear or cyclic aggregates (trimer, tetramer and polymer) were found from the resolution-enhanced spectra calculated by continuous wavelet transform. The changes of the eight species with concentration and temperature were analyzed using the intensity variation of the corresponding spectral features and two-dimensional correlation NIR spectroscopy. The aggregates were found to form at a very low concentration and the stability of the seven aggregates with temperature was found in an order of cyclic tetramer > linear polymer > linear tetramer > cyclic trimer > linear trimer > cyclic polymer > dimer. Furthermore, the formation of the aggregates was found to be affected by the chain length. The increase of the chain length is beneficial for the formation of cyclic tetramer and polymer due to the hydrophobic effect, but is an adverse effect for the formation of linear polymer.
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Affiliation(s)
- Yan Sun
- Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, PR China; Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, PR China; State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, PR China
| | - Xiaoyu Cui
- Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, PR China; Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, PR China; State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, PR China
| | - Wensheng Cai
- Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, PR China; Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, PR China; State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, PR China
| | - Xueguang Shao
- Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, PR China; Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, PR China; State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, PR China.
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Fernández Biscay C, Arini PD, Rincón Soler AI, Bonomini MP. Classification of ischemic and non-ischemic cardiac events in Holter recordings based on the continuous wavelet transform. Med Biol Eng Comput 2020; 58:1069-1078. [PMID: 32157593 DOI: 10.1007/s11517-020-02134-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 01/21/2020] [Indexed: 11/26/2022]
Abstract
Holter recordings are widely used to detect cardiac events that occur transiently, such as ischemic events. Much effort has been made to detect early ischemia, thus preventing myocardial infarction. However, after detection, classification of ischemia has still not been fully solved. The main difficulty relies on the false positives produced because of non-ischemic events, such as changes in the heart rate, the intraventricular conduction or the cardiac electrical axis. In this work, the classification of ischemic and non-ischemic events from the long-term ST database has been improved, using novel spectral parameters based on the continuous wavelet transform (CWT) together with temporal parameters (such as ST level and slope, T wave width and peak, R wave peak, QRS complex width). This was achieved by using a nearest neighbour classifier of six neighbours. Results indicated a sensitivity and specificity of 84.1% and 92.9% between ischemic and non-ischemic events, respectively, resulting a 10% increase of the sensitivity found in the literature. Extracted features based on the CWT applied on the ECG in the frequency band 0.5-4 Hz provided a substantial improvement in classifying ischemic and non-ischemic events, when comparing with the same classifier using only temporal parameters. Graphical Abstract In this work it is improved the classification of ischemic and non-ischemic events. The main difficulty of ischemic detectors relies on the false positives produced because of non-ischemic events. After a preprocessing stage, temporal and spectral parameters are extracted from events of the Long Term ST Database. The novel parameters proposed in this work are extracted from the Continuous Wavelet Transform. A nearest Neighbor Classifier is used, obtaining a sensitivity and specificity of 84.1% and 92.9%, respectively.
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Affiliation(s)
- Carolina Fernández Biscay
- Instituto Argentino de Matemática, "Alberto P. Calderón", CONICET, Saavedra 15, piso 3, Ciudad Autónoma de Buenos Aires, C1083ACA, Argentina.
- Instituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Paseo Colón 850, piso 4, Ciudad Autónoma de Buenos Aires, C1063ACV, Argentina.
| | - Pedro David Arini
- Instituto Argentino de Matemática, "Alberto P. Calderón", CONICET, Saavedra 15, piso 3, Ciudad Autónoma de Buenos Aires, C1083ACA, Argentina
- Instituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Paseo Colón 850, piso 4, Ciudad Autónoma de Buenos Aires, C1063ACV, Argentina
| | - Anderson Iván Rincón Soler
- Instituto Argentino de Matemática, "Alberto P. Calderón", CONICET, Saavedra 15, piso 3, Ciudad Autónoma de Buenos Aires, C1083ACA, Argentina
- Instituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Paseo Colón 850, piso 4, Ciudad Autónoma de Buenos Aires, C1063ACV, Argentina
| | - María Paula Bonomini
- Instituto Argentino de Matemática, "Alberto P. Calderón", CONICET, Saavedra 15, piso 3, Ciudad Autónoma de Buenos Aires, C1083ACA, Argentina
- Instituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Paseo Colón 850, piso 4, Ciudad Autónoma de Buenos Aires, C1063ACV, Argentina
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Chen X, Yin L, Fan Y, Song L, Ji T, Liu Y, Tian J, Zheng W. Temporal evolution characteristics of PM2.5 concentration based on continuous wavelet transform. Sci Total Environ 2020; 699:134244. [PMID: 31677460 DOI: 10.1016/j.scitotenv.2019.134244] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/12/2019] [Accepted: 09/01/2019] [Indexed: 05/27/2023]
Abstract
Fine particulate matter (PM2.5) is an important haze index, and the researches on the evolutionary characteristics of the PM2.5 concentration will provide a fundamental and guiding prerequisite for the haze prediction. However, the past researchers were usually based on the overall time-domain evolution information of PM2.5. Since the temporal evolution of PM2.5 concentration is nonstationary, previous studies might neglect some important localization features that the evolution has various predominant periods at different scales. Therefore, we applied the wavelet transform to study the localized intermittent oscillations of PM2.5. First, we analyze the daily average PM2.5 concentration collected from the automatic monitoring stations. The result reveals that the predominant oscillation period does vary with time. There exist multiple oscillation periods on the scale of 14-32 d, 62-104 d, 105-178 d and 216-389 d and the 298d is the first dominant period in the entire evolutionary process. Moreover, we want to figure out whether the temporal characteristics of PM2.5 in the days with heavy haze also have localized intermittent periodicities. We select the hourly average PM2.5 concentration in 120 h when the haze pollution is serious. We find that the principal period has experienced two abrupt shifts and the energy at the 63-hour scale is the most powerful. The results in these two independent analyses come into the same conclusion that the multiscale features shown in the temporal evolution of PM2.5 cannot be ignored and may play an important role in the further haze prediction.
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Affiliation(s)
- Xiaobing Chen
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China
| | - Lirong Yin
- Geographical and Sustainability Sciences Department, University of Iowa, Iowa City, IA 52242, USA
| | - Yulin Fan
- School of Foreign Languages, Sichuan Normal University, Chengdu 610101, PR China
| | - Lihong Song
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China
| | - Tingting Ji
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China
| | - Yan Liu
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China
| | - Jiawei Tian
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China
| | - Wenfeng Zheng
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China.
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Zhao Z, Deng Y, Zhang Y, Zhang Y, Zhang X, Shao L. DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. BMC Med Inform Decis Mak 2019; 19:286. [PMID: 31888592 PMCID: PMC6937790 DOI: 10.1186/s12911-019-1007-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 12/16/2019] [Indexed: 11/10/2022] Open
Abstract
Background Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. Methods In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. Results Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively Conclusions Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.
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Affiliation(s)
- Zhidong Zhao
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China. .,Hangdian Smart City Research Center of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China.
| | - Yanjun Deng
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
| | - Yang Zhang
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Yefei Zhang
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
| | - Xiaohong Zhang
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
| | - Lihuan Shao
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
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Zhou F, Li C, Yang C, Zhu H, Li Y. A spectrophotometric method for simultaneous determination of trace ions of copper, cobalt, and nickel in the zinc sulfate solution by ultraviolet-visible spectrometry. Spectrochim Acta A Mol Biomol Spectrosc 2019; 223:117370. [PMID: 31301648 DOI: 10.1016/j.saa.2019.117370] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 07/05/2019] [Accepted: 07/07/2019] [Indexed: 06/10/2023]
Abstract
In the zinc sulfate solution, the concentration ratio of zinc to metal ion impurities can be up to 105, which causes impurity ion signals to be severely masked by the zinc signal. In particular, nickel exhibits a strong nonlinearity. Conventional spectroscopic methods are commonly used to detect multi-component analytes with similar concentrations and require the detection component to be linear to satisfy Beer-Lambert law. In order to solve high concentration ratio and nonlinear problems, a spectrophotometric method combining the extended Kalman filter and derivative methods is proposed to simultaneously determine copper, cobalt and nickel in the zinc sulfate solution by ultraviolet-visible spectroscopy. The derivative method developed by using continuous wavelet transform with a Haar wavelet function was applied to detect copper and cobalt in regions with wavelengths greater than 500nm, in which the absorbance of zinc and nickel changed to a fixed value, where linear regression graphs for copper and cobalt were established at zero-crossing wavelengths. Extended Kalman filter spectrophotometry is a filtering algorithm for nonlinear systems, so it was proposed to iteratively detect nickel concentration. The detection range was found to be 0.5-5mg/L for copper, 0.3-3mg/L for cobalt, and 0.6-6mg/L. The predicted root mean square error was 0.097 for copper, 0.049 for cobalt, and 0.206 for nickel. The average relative deviations of copper, cobalt, and nickel in 10 sets of mixed solutions were 3.19%, 2.23%, and 4.56%, respectively. The spectrophotometric method studied is suitable for real-time detection and control of trace amounts of copper, cobalt, and nickel in purification process of zinc hydrometallurgy, and can be applied to more fields.
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Affiliation(s)
- Fengbo Zhou
- School of Physics and Electronics, Central South University, Changsha, Hunan 410083, PR China; School of Information Engineering, Shaoyang University, Shaoyang, Hunan 422000, PR China
| | - Changgeng Li
- School of Physics and Electronics, Central South University, Changsha, Hunan 410083, PR China
| | - Chunhua Yang
- School of Automation, Central South University, Changsha, Hunan 410083, PR China.
| | - Hongqiu Zhu
- School of Automation, Central South University, Changsha, Hunan 410083, PR China.
| | - Yonggang Li
- School of Automation, Central South University, Changsha, Hunan 410083, PR China
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Ugur TK, Erdamar A. An efficient automatic arousals detection algorithm in single channel EEG. Comput Methods Programs Biomed 2019; 173:131-138. [PMID: 31046987 DOI: 10.1016/j.cmpb.2019.03.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 03/01/2019] [Accepted: 03/18/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalographic arousal is a transient waveform that instantaneously happens in sleep as an inherent component. It has distinctive amplitude and frequency features. However, it is visually difficult to distinguish arousal from the background of the electroencephalogram. This visual scoring is important for brain researches, sleep studies, sleep stage scorings and assessment of sleep disorders. The scoring process is a time-consuming and difficult clinical procedure which is evaluated by sleep experts. It may also have subjective consequences due to the variability of personal expertise of physicians. Conversely, this scoring process can be significantly accelerated with computer-aided automated algorithms. Moreover, reproducible and objective results can be obtained. In this work, we propose a novel algorithm for the automatic detection of electroencephalographic arousals in sleep polysomnographic recordings. METHODS The approach uses a well-known time-frequency localization method, the continuous wavelet transform, to identify relevant arousal patterns. Special emphasis was carried out to produce a robust, reliable, fast and artifact tolerant algorithm. In the first part, the electroencephalographic scalogram, the squared magnitude of the continuous wavelet transform, was obtained. The mean and variance of the scalogram coefficients were determined as novel features. Support vector machine was applied as a classifier. Half of the recordings were used for training with five-fold cross-validation and a high accuracy training rate was obtained. Then, the rest of the recordings were used for testing. RESULTS As a result, the overall sensitivity, specificity, accuracy, and positive predictive value of the algorithm are 94.67%, 99.33%, 98.2%, and 97.93%, respectively. CONCLUSION In this paper, we have shown that the electroencephalographic arousal pattern can be characterized by the scalogram in the wavelet domain. The proposed algorithm works with high accuracy, reproducibility and gives objective results without case-specific sensitivity.
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Affiliation(s)
- Tugce Kantar Ugur
- Biomedical Engineering Department, Faculty of Engineering, Baskent University, Baglica Campus, 06790, Etimesgut, Ankara, Turkey.
| | - Aykut Erdamar
- Biomedical Engineering Department, Faculty of Engineering, Baskent University, Baglica Campus, 06790, Etimesgut, Ankara, Turkey.
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Shao X, Cui X, Wang M, Cai W. High order derivative to investigate the complexity of the near infrared spectra of aqueous solutions. Spectrochim Acta A Mol Biomol Spectrosc 2019; 213:83-89. [PMID: 30684883 DOI: 10.1016/j.saa.2019.01.059] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 12/19/2018] [Accepted: 01/15/2019] [Indexed: 06/09/2023]
Abstract
Derivative calculation is a powerful method for resolution enhancement in spectral analysis. A high order derivative method based on continuous wavelet transform (CWT) is discussed in the analysis of near infrared (NIR) spectra. The results for a simulated spectrum obtained from conventional numerical differentiation (NM), Fourier transform (FT), Savitzky-Golay (SG) and CWT method were compared. CWT method was found to be as efficient as FT and SG, but easier for high order derivative computation, and the fourth order derivative was proved to be a good choice for resolution enhancement as well as reduction of noise and sidelobe effects. For the NIR spectra of water-ethanol mixtures, the complexity of the spectra can be observed from the fourth derivative, including the spectral features of OH and CH with various intermolecular interactions. Fitting the derivative spectra of the mixtures by those of pure water and ethanol, the obtained coefficients for ethanol show a linear relation with the content but that for water exhibit a non-linear relation, which reveals the influence of ethanol on water structure in the mixture. Furthermore, the information of the water-ethanol clusters was found in the residual spectra after the fitting. Therefore, high order derivative can be an efficient way to improve the resolution of NIR spectra for understanding the interactions in aqueous solutions.
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Affiliation(s)
- Xueguang Shao
- Xinjiang Laboratory of Native Medicinal and Edible Plant Resources Chemistry, College of Chemistry and Environmental Science, Kashgar University, Kashgar 844006, China; Research Center for Analytical Sciences, College of Chemistry, Nankai University. Tianjin 300071, China; Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, China; State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300071, China.
| | - Xiaoyu Cui
- Research Center for Analytical Sciences, College of Chemistry, Nankai University. Tianjin 300071, China
| | - Mian Wang
- Research Center for Analytical Sciences, College of Chemistry, Nankai University. Tianjin 300071, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, College of Chemistry, Nankai University. Tianjin 300071, China
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37
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Zhang X, Liu Z, Wang J, Wang J. Time-frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets. ISA Trans 2019; 87:225-234. [PMID: 30528123 DOI: 10.1016/j.isatra.2018.11.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 10/23/2018] [Accepted: 11/26/2018] [Indexed: 06/09/2023]
Abstract
Rolling element bearings are key and also vulnerable machine elements in rotating machinery. Fault diagnosis of rolling element bearings is significant for guaranteeing machinery safety and functionality. To accurately extract bearing diagnostic information, a time-frequency analysis method based on continuous wavelet transform (CWT) and multiple Q-factor Gabor wavelets (MQGWs) (termed CMQGWT) is introduced in this paper. In the CMQGWT method, Gabor wavelets with multiple Q-factors are adopted and sets of the continuous wavelet coefficients for each Q-factor are combined to generate time-frequency map. By this way, the resolution of the CWT time-frequency map can be greatly increased and the diagnostic information can be accurately identified. Numerical simulation is carried out and verified the effectiveness of the proposed method. Case studies and comparisons with the continuous Morlet wavelet transform (CMWT) and the tunable Q-factor wavelet transform (TQWT) demonstrate the effectiveness and superiority of the CMQGWT for bearing diagnostic information extraction and fault identification.
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Affiliation(s)
- Xin Zhang
- School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610065, PR China.
| | - Zhiwen Liu
- School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610065, PR China.
| | - Jiaxu Wang
- School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610065, PR China.
| | - Jinglin Wang
- Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management, AVIC Shanghai Aeronautical Measurement & Controlling Research Institute, Shanghai 201601, PR China
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38
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Zhang S, Shen Q, Nie C, Huang Y, Wang J, Hu Q, Ding X, Zhou Y, Chen Y. Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods. Spectrochim Acta A Mol Biomol Spectrosc 2019; 211:393-400. [PMID: 30594866 DOI: 10.1016/j.saa.2018.12.032] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 12/07/2018] [Accepted: 12/17/2018] [Indexed: 06/09/2023]
Abstract
Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. We explored reflectance spectroscopy as an alternative method for assessing heavy metals. Four spectral transformation methods, first-order differential (FDR), second-order differential (SDR), continuum removal (CR) and continuous wavelet transform (CWT), are used for the original spectral data. Spectral preprocessing effectively eliminated the noise and baseline drifting and also highlighted the locations of the spectral feature bands. Partial least squares regression (PLSR) and radial basis function neural network (RBF) were used to study the hyperspectral inversion of four heavy metals (Cr, As, Ni, Cd). The inversion models of four heavy metals were established in the bands with the highest correlation coefficient. The inversion effects were evaluated by the coefficient of determination (R2), root mean square error (RMSE) and residual predictive deviation (RPD) indexes. The R values of the correlation coefficient were significantly improved after smoothing and spectral transformation compared to the original waveband. The method combining continuous wavelet transform (CWT) with radial basis function neural network (RBF) had the best inversion effect on the four heavy metals. When compared to partial least squares regression (PLSR), the RMSE values were reduced by approximately 2. The CWT-RBF method can be used as a means of inversion of heavy metals in mining wasteland reclaimed land.
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Affiliation(s)
- Shiwen Zhang
- College of Earth and Environmental Science, Anhui University of Science and Technology, Huainan, China.
| | - Qiang Shen
- Faculty of Surveying and Mapping, Anhui University of Science and Technology, Huainan, China.
| | - Chaojia Nie
- College of Earth and Environmental Science, Anhui University of Science and Technology, Huainan, China
| | - Yuanfang Huang
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Jianhua Wang
- No. 1 Engineering Company Ltd. of CCCC First Harbor Engineering Company Ltd, Tianjin, China
| | - Qingqing Hu
- College of Earth and Environmental Science, Anhui University of Science and Technology, Huainan, China
| | - Xuejiao Ding
- College of Earth and Environmental Science, Anhui University of Science and Technology, Huainan, China
| | - Yan Zhou
- Land Consolidation and Rehabilitation Center of the Ministry of Natural Resource, Beijing, China.
| | - Yuanpeng Chen
- Land Consolidation and Rehabilitation Center of the Ministry of Natural Resource, Beijing, China
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Salama FMM, Attia KAM, Said RAM, El-Zeiny MB, El-Attar AAMM. A comparative study of different aspects in manipulating ratio spectra used for the analysis of cefradine in the presence of its alkaline degradation product. Spectrochim Acta A Mol Biomol Spectrosc 2019; 207:105-111. [PMID: 30212663 DOI: 10.1016/j.saa.2018.08.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 08/13/2018] [Accepted: 08/26/2018] [Indexed: 06/08/2023]
Abstract
Six stability-indicating UV-spectrophotometric methods manipulating ratio spectra were utilized for the analysis of cefradine in presence of its alkaline degradate. These methods are different forms of transformations; ratio difference, mean centering, derivative ratio using numerical differentiation, derivative ratio using Savitsky-Golay filter, continuous wavelet transform and derivative continuous wavelet transform. Water was used as a solvent and the linearity ranges were 6-26 μg/mL. Determination of accuracy and precision for the suggested procedures were executed. Assessment of specificity was run through analyzing laboratory prepared mixtures containing cefradine and its alkaline degradate. The suggested methods were useful for cefradine estimation in tablets. Statistically, the outputs obtained from the recommended and published methods reveal no significant differences.
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Affiliation(s)
- Fathy M M Salama
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Khalid A M Attia
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Ragab A M Said
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Mohamed B El-Zeiny
- Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), 12582 Al Hadaba Al Wosta, Cairo, Egypt
| | - Abdul-Aziz M M El-Attar
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt.
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Zuzarte I, Indic P, Sternad D, Paydarfar D. Quantifying Movement in Preterm Infants Using Photoplethysmography. Ann Biomed Eng 2018; 47:646-658. [PMID: 30255214 DOI: 10.1007/s10439-018-02135-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 09/18/2018] [Indexed: 10/28/2022]
Abstract
Long-term recordings of movement in preterm infants might reveal important clinical information. However, measurement of movement is limited because of time-consuming and subjective analysis of video or reluctance to attach additional sensors to the infant. We evaluated whether photoplethysmogram (PPG), routinely used for oximetry in preterm infants in the neonatal intensive care unit (NICU), can provide reliable long-term measurements of movement. In 18 infants [mean post-conceptional age (PCA) 31.10 weeks, range 29-34.29 weeks], we designed and tested a wavelet-based algorithm that detects movement signals from the PPG. The algorithm's performance was optimized relative to subjective assessments of movement using video and accelerometers attached to two limbs and force sensors embedded within the mattress (five infants, three raters). We then applied the optimized algorithm to infants receiving routine care in the NICU without additional sensors. The algorithm revealed a decline in brief movements (< 5 s) with increasing PCA (13 infants, r = - 0.87, p < 0.001, PCA range 27.3-33.9 weeks). Our findings suggest that quantitative relationships between motor activity and clinical outcomes in preterm infants can be studied using routine photoplethysmography.
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Affiliation(s)
- Ian Zuzarte
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Premananda Indic
- Department of Electrical Engineering, University of Texas, Tyler, TX, USA
| | - Dagmar Sternad
- Departments of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, Boston, MA, USA
| | - David Paydarfar
- Department of Neurology, Dell Medical School, and Institute for Computational Engineering and Sciences, The University of Texas, 1701 Trinity St. Stop Z0700, Health Discovery Bldg, 5.708A, Austin, TX, 78712, USA.
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Sohrabi MR, Mirzabeygi V, Davallo M. Use of continuous wavelet transform approach for simultaneous quantitative determination of multicomponent mixture by UV-Vis spectrophotometry. Spectrochim Acta A Mol Biomol Spectrosc 2018; 201:306-314. [PMID: 29763824 DOI: 10.1016/j.saa.2018.05.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 05/04/2018] [Accepted: 05/06/2018] [Indexed: 06/08/2023]
Abstract
In the present paper, a multicomponent analysis approach based on spectrophotometry method was developed for simultaneous determination of Guaifenesin (GU), Chlorpheniramine (CHL) and Pseudoephedrine (PSE) without any separation steps. The method under study is signal processing method based on Continuous Wavelet Transform (CWT) coupled with zero cross point technique. In this paper, CWT method was tested by synthetic ternary mixtures and was applied to the commercial cough syrup as a real sample and assessed by applying the standard addition technique. For demonstrate the accuracy of the results, other applications of signal processing, such as Derivative Transform (DT), Partial Least Squares (PLS) regression and Principal Components Regression (PCR) were used as comparative methods. Afterwards, the obtained results from analyzing the cough syrup by all methods were compared to the High-Performance Liquid Chromatography (HPLC) as a reference method. One-way analysis of variance test at 95% confidence level showed no significant differences between CWT and other applications.
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Affiliation(s)
- Mahmoud Reza Sohrabi
- Department of Chemistry, Faculty of Chemistry, Azad University, North Tehran Branch, P.O. Box 1913674711, Tehran, Iran.
| | - Vahid Mirzabeygi
- Department of Chemistry, Faculty of Chemistry, Azad University, North Tehran Branch, P.O. Box 1913674711, Tehran, Iran
| | - Mehran Davallo
- Department of Chemistry, Faculty of Chemistry, Azad University, North Tehran Branch, P.O. Box 1913674711, Tehran, Iran
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Drewnik M, Rajwa-Kuligiewicz A, Stolarczyk M, Kucharzyk S, Żelazny M. Intra-annual groundwater levels and water temperature patterns in raised bogs affected by human impact in mountain areas in Poland. Sci Total Environ 2018; 624:991-1003. [PMID: 29929269 DOI: 10.1016/j.scitotenv.2017.12.203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/01/2017] [Accepted: 12/18/2017] [Indexed: 06/08/2023]
Abstract
Over the last century, the vast majority of peatlands in Europe have experienced substantial transformation as a result of drainage works that led to an imbalance in the natural hydrologic regime as well as changes in vegetation composition. The ongoing study aims to reconstruct the natural hydrologic regime of peatlands and restore their typical vegetation communities. In this study, we examine the variability of groundwater levels and groundwater temperature in raised bogs located in the Bieszczady Mts. in southern Poland. Both groundwater table levels and groundwater temperature serve to characterise the hydrology of peatlands, which in turn is critical for plant growth and rates of relevant biochemical processes. Our objective is to determine the predominant scale of intra-annual variability in time series and identify their potential sources by assessing the adaptive response of peat bogs to key changes in weather conditions. For this purpose, data obtained from 9 monitoring wells located in peat bogs, with a varying degree of degradation, were used. Fluctuations in time series and potential linkages between selected variables were analysed in the frequency domain using the continuous wavelet transform. The results show that peat bogs exhibit a relatively high stability of groundwater table levels and groundwater temperature despite meaningful changes in weather conditions. The most visible response of peat bogs to weather conditions was observed in summer and autumn. Our study demonstrates that degraded peat bogs experience the largest decrease in groundwater table levels and more frequent fluctuations. In contrast, groundwater temperature remained stable throughout the year at all the studied bog sites.
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Affiliation(s)
- Marek Drewnik
- Institute of Geography and Spatial Management, 30-387 Cracow, Gronostajowa Str. 7, Poland.
| | | | - Mateusz Stolarczyk
- Institute of Geography and Spatial Management, 30-387 Cracow, Gronostajowa Str. 7, Poland.
| | | | - Mirosław Żelazny
- Institute of Geography and Spatial Management, 30-387 Cracow, Gronostajowa Str. 7, Poland.
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Ainalis D, Ducarne L, Kaufmann O, Tshibangu JP, Verlinden O, Kouroussis G. Improved analysis of ground vibrations produced by man-made sources. Sci Total Environ 2018; 616-617:517-530. [PMID: 29132126 DOI: 10.1016/j.scitotenv.2017.10.291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 10/27/2017] [Accepted: 10/27/2017] [Indexed: 06/07/2023]
Abstract
Man-made sources of ground vibration must be carefully monitored in urban areas in order to ensure that structural damage and discomfort to residents is prevented or minimised. The research presented in this paper provides a comparative evaluation of various methods used to analyse a series of tri-axial ground vibration measurements generated by rail, road, and explosive blasting. The first part of the study is focused on comparing various techniques to estimate the dominant frequency, including time-frequency analysis. The comparative evaluation of the various methods to estimate the dominant frequency revealed that, depending on the method used, there can be significant variation in the estimates obtained. A new and improved analysis approach using the continuous wavelet transform was also presented, using the time-frequency distribution to estimate the localised dominant frequency and peak particle velocity. The technique can be used to accurately identify the level and frequency content of a ground vibration signal as it varies with time, and identify the number of times the threshold limits of damage are exceeded.
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Affiliation(s)
- Daniel Ainalis
- Department of Theoretical Mechanics, Dynamics and Vibrations, University of Mons, Belgium.
| | - Loïc Ducarne
- Department of Theoretical Mechanics, Dynamics and Vibrations, University of Mons, Belgium
| | - Olivier Kaufmann
- Department of Geology and Applied Geology, University of Mons, Belgium
| | | | - Olivier Verlinden
- Department of Theoretical Mechanics, Dynamics and Vibrations, University of Mons, Belgium
| | - Georges Kouroussis
- Department of Theoretical Mechanics, Dynamics and Vibrations, University of Mons, Belgium
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Hassan SA, Abdel-Gawad SA. Application of wavelet and Fuorier transforms as powerful alternatives for derivative spectrophotometry in analysis of binary mixtures: A comparative study. Spectrochim Acta A Mol Biomol Spectrosc 2018; 191:365-371. [PMID: 29055281 DOI: 10.1016/j.saa.2017.08.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 07/29/2017] [Accepted: 08/13/2017] [Indexed: 06/07/2023]
Abstract
Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS. The three signal processing methods were adopted for the quantification of NTX and BUP in pure and tablet forms. The adopted methods were validated according to the ICH guideline where accuracy, precision and specificity were found to be within appropriate limits.
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Affiliation(s)
- Said A Hassan
- Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo ET-11562, Egypt.
| | - Sherif A Abdel-Gawad
- Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo ET-11562, Egypt; Pharmaceutical Chemistry Department, College of Pharmacy, Prince Sattam Bin-Abdul Aziz University, Al-Kharj, 11942, Saudi Arabia
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Attia KAM, El-Abasawi NM, El-Olemy A, Serag A. Different spectrophotometric methods applied for the analysis of simeprevir in the presence of its oxidative degradation product: Acomparative study. Spectrochim Acta A Mol Biomol Spectrosc 2018; 190:1-9. [PMID: 28889051 DOI: 10.1016/j.saa.2017.08.066] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 08/05/2017] [Accepted: 08/31/2017] [Indexed: 06/07/2023]
Abstract
Five simple spectrophotometric methods were developed for the determination of simeprevir in the presence of its oxidative degradation product namely, ratio difference, mean centering, derivative ratio using the Savitsky-Golay filters, second derivative and continuous wavelet transform. These methods are linear in the range of 2.5-40μg/mL and validated according to the ICH guidelines. The obtained results of accuracy, repeatability and precision were found to be within the acceptable limits. The specificity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. Furthermore, these methods were statistically comparable to RP-HPLC method and good results were obtained. So, they can be used for the routine analysis of simeprevir in quality-control laboratories.
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Affiliation(s)
- Khalid A M Attia
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Nasr M El-Abasawi
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Ahmed El-Olemy
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt
| | - Ahmed Serag
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt.
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Anastasiadou MN, Christodoulakis M, Papathanasiou ES, Papacostas SS, Mitsis GD. Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests. Clin Neurophysiol 2017; 128:1755-1769. [PMID: 28778057 DOI: 10.1016/j.clinph.2017.06.247] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Revised: 05/19/2017] [Accepted: 06/20/2017] [Indexed: 11/15/2022]
Abstract
OBJECTIVE This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). METHODS The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. RESULTS We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). CONCLUSION The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. SIGNIFICANCE Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection.
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Affiliation(s)
- Maria N Anastasiadou
- McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada
| | - Manolis Christodoulakis
- McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada
| | - Eleftherios S Papathanasiou
- McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada
| | - Savvas S Papacostas
- McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada
| | - Georgios D Mitsis
- McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada.
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Ibrahim MM, Elzanfaly ES, El-Zeiny MB, Ramadan NK, Kelani KM. Spectrophotometric determination of meclizine hydrochloride and pyridoxine hydrochloride in laboratory prepared mixtures and in their pharmaceutical preparation. Spectrochim Acta A Mol Biomol Spectrosc 2017; 178:234-238. [PMID: 28199928 DOI: 10.1016/j.saa.2017.02.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Revised: 02/02/2017] [Accepted: 02/05/2017] [Indexed: 06/06/2023]
Abstract
In this paper, three rapid, simple, accurate and precise spectrophotometric methods were developed for the determination of meclizine hydrochloride in the presence of pyridoxine hydrochloride without previous separation. The methods under study are dual wavelength (DWL), ratio difference (RD) and continuous wavelet transform (CWT). On the other hand, pyridoxine hydrochloride (PYH) was determined directly at 291nm. The methods obey Beer's law in the range of (5-50μg/mL) for both compounds. All the methods were validated according to the ICH guidelines where the accuracy was found to be 98.29, 99.59, 100.42 and 100.62% for DWL, RD, CWT and PYH; respectively. Moreover the precision of the methods were calculated in terms of %RSD and it was found to be 0.545, 0.372, 1.287 and 0.759 for DWL, RD,CWT and PYH; respectively. The selectivity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of pyridoxine hydrochloride and meclizine hydrochloride in quality-control laboratories.
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Affiliation(s)
- Maha M Ibrahim
- Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Egypt
| | - Eman S Elzanfaly
- Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Egypt
| | - Mohamed B El-Zeiny
- Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Egypt.
| | - Nesreen K Ramadan
- Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Egypt
| | - Khadiga M Kelani
- Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Egypt; Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Egypt
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Fan M, Cai W, Shao X. Investigating the Structural Change in Protein Aqueous Solution Using Temperature-Dependent Near-Infrared Spectroscopy and Continuous Wavelet Transform. Appl Spectrosc 2017; 71:472-479. [PMID: 27650983 DOI: 10.1177/0003702816664103] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The circulatory protein, human serum albumin (HSA), is widely used as a model protein for the study of protein structure. In this work, the structures of human serum albumin in aqueous solutions are studied using temperature-dependent near-infrared (NIR) spectroscopy with the aid of continuous wavelet transform (CWT). Near-infrared spectra of human serum albumin solutions with different concentrations were measured over a temperature range of 30-85 ℃. Then, continuous wavelet transform was performed on the spectra to enhance the resolution. As a result of the resolution enhancement, spectral bands around 4361, 4521, 4600 and 4260 cm-1 were extracted from the overlapping low-resolution signals. The four bands can be assigned to the protein structures of α-helix, β-sheet, an intermediate state and side chains, respectively. The variations in intensity of the bands around 4361 and 4521 cm-1 with temperature show that the increase of temperature leads to the loss of α-helical structure but the formation of β-sheet, and the denaturation temperature of human serum albumin is about 55 ℃. The variation of the band around 4600 cm-1 indicates that the temperature-induced unfolding process of human serum albumin occurs through a stable intermediate state, and a significant change in the microenvironment of the side chains about 63 ℃ is observed from the variation of the band around 4260 cm-1. On the other hand, the transformed spectra in the region of 8000-5600 cm-1 provide an explicit evidence for the structural changes of water during the process of protein denaturation, and the unfolding process of HSA can be reflected by these changes.
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Affiliation(s)
- Mengli Fan
- 1 Research Center for Analytical Sciences, Nankai University, China
| | - Wensheng Cai
- 1 Research Center for Analytical Sciences, Nankai University, China
| | - Xueguang Shao
- 1 Research Center for Analytical Sciences, Nankai University, China
- 2 Tianjin Key Laboratory of Biosensing and Molecular Recognition, China
- 3 State Key Laboratory of Medicinal Chemical Biology, China
- 4 Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), China
- 5 College of Chemistry and Environmental Science, Kashgar University, China
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Huang YA, You ZH, Chen X, Yan GY. Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC Syst Biol 2016; 10:120. [PMID: 28155718 PMCID: PMC5260127 DOI: 10.1186/s12918-016-0360-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Background Protein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological technologies can be used to identify new PPIs, but they are expensive, time-consuming, and tedious. Therefore, computational methods for predicting PPIs have an important role. For the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting PPIs. The major limitation in principle of these methods lies in the prior information of the protein to infer PPIs. Therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence. Results Here, we report a highly efficient approach for predicting PPIs. The main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and Chou’s pseudo amino acid composition (PseAAC), and from adopting weighted sparse representation based classifier (WSRC). This method, cross-validated on the PPIs datasets of Saccharomyces cerevisiae, Human and H. pylori, achieves an excellent results with accuracies as high as 92.50%, 95.54% and 84.28% respectively, significantly better than previously proposed methods. Extensive experiments are performed to compare the proposed method with state-of-the-art Support Vector Machine (SVM) classifier. Conclusions The outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. In addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and WSRC. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.
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Affiliation(s)
- Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.
| | - Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Gui-Ying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100010, China
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Olesen SS, Gram M, Jackson CD, Halliday E, Sandberg TH, Drewes AM, Morgan MY. Electroencephalogram variability in patients with cirrhosis associates with the presence and severity of hepatic encephalopathy. J Hepatol 2016; 65:517-23. [PMID: 27184531 DOI: 10.1016/j.jhep.2016.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 04/19/2016] [Accepted: 05/07/2016] [Indexed: 01/24/2023]
Abstract
BACKGROUND & AIMS The outputs of physiological systems fluctuate in a complex manner even under resting conditions. Decreased variability or increased regularity of these outputs is documented in several disease states. Changes are observed in the spatial and temporal configuration of the electroencephalogram (EEG) in patients with hepatic encephalopathy (HE), but there is no information on the variability of the EEG signal in this condition. The aim of this study was to measure and characterize EEG variability in patients with cirrhosis and to determine its relationship to neuropsychiatric status. METHODS Eyes-closed, awake EEGs were obtained from 226 patients with cirrhosis, classified, using clinical and psychometric criteria, as neuropsychiatrically unimpaired (n=127) or as having minimal (n=21) or overt (n=78) HE, and from a reference population of 137 healthy controls. Analysis of EEG signal variability was undertaken using continuous wavelet transform and sample entropy. RESULTS EEG variability was reduced in the patients with cirrhosis compared with the reference population (coefficient of variation: 21.2% [19.3-23.4] vs. 22.4% [20.8-24.5]; p<0.001). A significant association was observed between EEG variability and neuropsychiatric status; thus, variability was increased in the patients with minimal HE compared with their neuropsychiatrically unimpaired counterparts (sample entropy: 0.98 [0.87-1.14] vs. 0.83 [0.75-0.95]; p=0.02), and compared with the patients with overt HE (sample entropy: 0.98 [0.87-1.14] vs. 0.82 [0.71-1.01]; p=0.01). CONCLUSIONS Variability of the EEG is associated with both the presence and severity of HE. This novel finding may provide new insights into the pathophysiology of HE and provide a means for monitoring patients over time. LAY SUMMARY Decreased variability or increased regularity of physiological systems is documented in several disease states. Variability of the electroencephalogram was found to be associated with both the presence and severity of brain dysfunction in patients with chronic liver disease.
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Affiliation(s)
- Søren Schou Olesen
- Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark.
| | - Mikkel Gram
- Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark
| | - Clive Douglas Jackson
- Department of Neurophysiology, Royal Free Hospital, Royal Free London NHS Foundation Trust, Hampstead, London, UK
| | - Edwin Halliday
- UCL Institute for Liver and Digestive Health, Division of Medicine, Royal Free Campus, University College London, Hampstead, London, UK
| | - Thomas Holm Sandberg
- Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark
| | - Asbjørn Mohr Drewes
- Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Marsha Yvonne Morgan
- UCL Institute for Liver and Digestive Health, Division of Medicine, Royal Free Campus, University College London, Hampstead, London, UK
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