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Liu CH, Peng CH, Huang LY, Chen FY, Kuo CH, Wu CZ, Cheng YF. Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients. BMC Neurol 2024; 24:11. [PMID: 38166825 PMCID: PMC10759520 DOI: 10.1186/s12883-023-03507-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
INTRODUCTION The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D. METHODS We recruited older T2D between 60-95 years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost). RESULTS Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance. CONCLUSION In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.
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Affiliation(s)
- Chi-Hao Liu
- Department of Medicine, Division of Nephrology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, R.O.C
| | - Chung-Hsin Peng
- Department of Urology, Cardinal Tien Hospital, School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan, R.O.C
| | - Li-Ying Huang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C
| | - Fang-Yu Chen
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Fu Jen Catholic University Hospital, New Taipei City, Taiwan, R.O.C
| | - Chun-Heng Kuo
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C
| | - Chung-Ze Wu
- Department of Internal Medicine, Division of Endocrinology, Shuang Ho Hospital, New Taipei City, 23561, R.O.C
- Division of Endocrinology and Metabolism, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan, R.O.C
| | - Yu-Fang Cheng
- Department of Endocrinology and Metabolism, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua City, 50006, Taiwan, R.O.C..
- Department of Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C..
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Jiahao L, Shuixian L, Keshun Y, Bohua Z. An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction. Phys Eng Sci Med 2023; 46:1341-1352. [PMID: 37393423 DOI: 10.1007/s13246-023-01286-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/22/2023] [Indexed: 07/03/2023]
Abstract
This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases.
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Affiliation(s)
- Li Jiahao
- Ganzhou Polytechnic, Zhanggong District, Ganzhou City, 341099, Jiangxi Province, China
| | - Luo Shuixian
- The First Affiliated Hospital of Gannan Medical College, No. 23, Qingnian Road, Ganzhou City, 341001, Jiangxi Province, China
| | - You Keshun
- Jiangxi University of Science and Technology, 1958 Hakka Avenue, Ganzhou City, 341000, Jiangxi Province, China.
| | - Zen Bohua
- Ganzhou Polytechnic, Zhanggong District, Ganzhou City, 341099, Jiangxi Province, China
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Rahman MM, Rivolta MW, Badilini F, Sassi R. A Systematic Survey of Data Augmentation of ECG Signals for AI Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115237. [PMID: 37299964 DOI: 10.3390/s23115237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
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Affiliation(s)
- Md Moklesur Rahman
- Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
| | | | - Fabio Badilini
- School of Nursing, University of California, San Francisco, CA 94143, USA
- AMPS-LLC, New York, NY 10025, USA
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
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Healthcare Engineering JO. Retracted: An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:9862375. [PMID: 37266292 PMCID: PMC10232119 DOI: 10.1155/2023/9862375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/03/2023]
Abstract
[This retracts the article DOI: 10.1155/2022/3408501.].
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Alphonse AS, Benifa JVB, Muaad AY, Chola C, Heyat MBB, Murshed BAH, Abdel Samee N, Alabdulhafith M, Al-antari MA. A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images. Diagnostics (Basel) 2023; 13:diagnostics13061104. [PMID: 36980412 PMCID: PMC10047753 DOI: 10.3390/diagnostics13061104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.
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Affiliation(s)
- A. Sherly Alphonse
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - J. V. Bibal Benifa
- Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - Channabasava Chola
- Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | | | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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Ullah H, Heyat MBB, Akhtar F, Muaad AY, Ukwuoma CC, Bilal M, Miraz MH, Bhuiyan MAS, Wu K, Damaševičius R, Pan T, Gao M, Lin Y, Lai D. An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal. Diagnostics (Basel) 2022; 13:diagnostics13010087. [PMID: 36611379 PMCID: PMC9818233 DOI: 10.3390/diagnostics13010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/05/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.
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Affiliation(s)
- Hadaate Ullah
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | | | - Chiagoziem C. Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Muhammad Bilal
- College of Pharmacy, Liaquat University of Medical and Health Sciences, Jamshoro 76090, Pakistan
| | - Mahdi H. Miraz
- School of Computing and Data Science, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Malaysia
- School of Computing, Glyndŵr University, Wrexham LL11 2AW, UK
| | | | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Taisong Pan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Min Gao
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan Lin
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Corporation on Applied Medicine Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Dakun Lai
- Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
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Pal R, Adhikari D, Heyat MBB, Guragai B, Lipari V, Brito Ballester J, De la Torre Díez I, Abbas Z, Lai D. A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students' Cardiac Signal and MSY. Bioengineering (Basel) 2022; 9:bioengineering9120793. [PMID: 36550999 PMCID: PMC9774730 DOI: 10.3390/bioengineering9120793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.
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Affiliation(s)
- Rishi Pal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Deepak Adhikari
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
- Correspondence: (M.B.B.H.); (D.L.)
| | - Bishal Guragai
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Vivian Lipari
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Europea Del Atlántico, Isabel Torres, 39011 Santander, Spain
- Research Group on Foods, Nutritional Biochemistry and Health Universidade Internacional do Cuanza, Cuito EN250, Angola
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Julien Brito Ballester
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Europea Del Atlántico, Isabel Torres, 39011 Santander, Spain
- Research Group on Foods, Nutritional Biochemistry and Health Universidade Internacional do Cuanza, Cuito EN250, Angola
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Isabel De la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Zia Abbas
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
| | - Dakun Lai
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
- Correspondence: (M.B.B.H.); (D.L.)
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Said RR, Heyat MBB, Song K, Tian C, Wu Z. A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials. BIOSENSORS 2022; 12:bios12121134. [PMID: 36551100 PMCID: PMC9776155 DOI: 10.3390/bios12121134] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/24/2022] [Accepted: 12/02/2022] [Indexed: 06/01/2023]
Abstract
To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.
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Affiliation(s)
- Ramadhan Rashid Said
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Keer Song
- Franklin College of Arts and Science, University of Georgia, Athens, GA 30602, USA
| | - Chao Tian
- Department of Women’s Health, Sichuan Cancer Hospital, Chengdu 610044, China
| | - Zhe Wu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
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Bin Heyat MB, Akhtar F, Sultana A, Tumrani S, Teelhawod BN, Abbasi R, Amjad Kamal M, Muaad AY, Lai D, Wu K. Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning. Curr Pharm Des 2022; 28:3618-3636. [PMID: 36464881 DOI: 10.2174/1381612829666221201161636] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/20/2022] [Accepted: 10/27/2022] [Indexed: 12/07/2022]
Abstract
Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.
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Affiliation(s)
- Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Arshiya Sultana
- Department of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Bengaluru, Karnataka, India
| | - Saifullah Tumrani
- Department of Computer Science, Bahria University, Karachi 75260, Pakistan
| | - Bibi Nushrina Teelhawod
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Rashid Abbasi
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.,King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia.,Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh.,Enzymoics, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Abdullah Y Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India.,Sana'a Community College, Sana'a 5695, Yemen
| | - Dakun Lai
- BMI-EP Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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11
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Chola C, Muaad AY, Bin Heyat MB, Benifa JVB, Naji WR, Hemachandran K, Mahmoud NF, Samee NA, Al-Antari MA, Kadah YM, Kim TS. BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification. Diagnostics (Basel) 2022; 12:diagnostics12112815. [PMID: 36428875 PMCID: PMC9689932 DOI: 10.3390/diagnostics12112815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/03/2022] [Accepted: 11/12/2022] [Indexed: 11/19/2022] Open
Abstract
Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet's architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.
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Affiliation(s)
- Channabasava Chola
- Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - J. V. Bibal Benifa
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kerala 686635, India
| | - Wadeea R. Naji
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - K. Hemachandran
- Department of Artificial Intelligence, Woxsen University, Hyderabad 502345, India
| | - Noha F. Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Mugahed A. Al-Antari
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Yasser M. Kadah
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Biomedical Engineering Department, Cairo University, Giza 12613, Egypt
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Tae-Seong Kim
- Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
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12
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An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9475162. [PMID: 36210977 PMCID: PMC9536938 DOI: 10.1155/2022/9475162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/25/2022] [Accepted: 08/17/2022] [Indexed: 01/10/2023]
Abstract
Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.
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13
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Ullah H, Wahab MA, Will G, Karim MR, Pan T, Gao M, Lai D, Lin Y, Miraz MH. Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring. BIOSENSORS 2022; 12:bios12080630. [PMID: 36005025 PMCID: PMC9406032 DOI: 10.3390/bios12080630] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/22/2022] [Accepted: 07/27/2022] [Indexed: 05/27/2023]
Abstract
Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the four different sensors, i.e., piezoresistive, piezoelectric, iontronic, and capacitive, capacitive sensors are the most advantageous owing to their reusability, high durability, device sterilization ability, and minimum leakage currents between the electrode and the body to reduce the health risk arising from any short circuit. This review focuses on the development of wearable, flexible capacitive sensors for monitoring electrophysiological conditions, including the electrode materials and configuration, the sensing mechanisms, and the fabrication strategies. In addition, several design strategies of flexible/stretchable electrodes, body-to-electrode signal transduction, and measurements have been critically evaluated. We have also highlighted the gaps and opportunities needed for enhancing the suitability and practical applicability of wearable capacitive sensors. Finally, the potential applications, research challenges, and future research directions on stretchable and wearable capacitive sensors are outlined in this review.
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Affiliation(s)
- Hadaate Ullah
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Md A. Wahab
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
- School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, George St Brisbane, GPO Box 2434, Brisbane, QLD 4001, Australia
| | - Geoffrey Will
- School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, George St Brisbane, GPO Box 2434, Brisbane, QLD 4001, Australia
| | - Mohammad R. Karim
- Center of Excellence for Research in Engineering Materials (CEREM), Deanship of Scientific Research (DSR), King Saud University, Riyadh 11421, Saudi Arabia
- K.A. CARE Energy Research and Innovation Center, Riyadh 11451, Saudi Arabia
| | - Taisong Pan
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Min Gao
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dakun Lai
- Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan Lin
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Corporation on Applied Medicine Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mahdi H. Miraz
- School of Computing and Data Science, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Malaysia
- School of Computing, Faculty of Arts, Science and Technology, Wrexham Glyndŵr University, Wrexham LL112AW, UK
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