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Shcherban IV, Fedotova VS, Matukhno AE, Shepelev IE, Shcherban OG, Lysenko LV. A method for detecting spatiotemporal patterns of cancer biomarkers-evoked activity using radial basis function network extracted time-domain features from calcium imaging data. J Neurosci Methods 2024; 405:110097. [PMID: 38408525 DOI: 10.1016/j.jneumeth.2024.110097] [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: 09/06/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Two-photon calcium imaging is widely used to study the odor-evoked glomerular activity in the dorsal olfactory bulb of macrosmatic animals. The nonstationary character of activated patterns sets a limit on the use of a traditional image processing approaches. NEW METHOD The developed method makes it possible to automatically map cancer biomarkers-activated glomeruli in the rat dorsal olfactory bulb. We interpolated fluorescence intensity of calcium dynamics based on the Gaussian RBF network and synthesized the physiological fluorescence model of the receptive glomerular field. RESULTS The experiments on 5 rats confirmed the correctness of the developed approach. Patterns evoked by the 6-methyl-5-hepten-2-one (stomach cancer biomarker) and benzene (lung cancer biomarker) were correctly identified. COMPARISON WITH EXISTING METHODS The proposed method was compared with the nonnegative matrix factorization method and with the method based on computer vision algorithms. The developed approach showed better accuracy in experiments and provided the mathematical models of the odor-evoked patterns synthesis. These models can be used to generate synthetic images of odor-evoked glomerular activity and thus to overcome the problem of small experimental data collected in calcium imaging. CONCLUSIONS The proposed method should be considered part of the toolkit for fully automatic analysis of calcium imaging-based studies. Currently available methodology is not able to use breath biomarkers to reliably discriminate between cancer patients and healthy controls. Nevertheless, the effective identification of the spatial patterns of cancer biomarkers-evoked glomerular activity can serve as the foundation for highly sensitive biohybrid systems for cancer screening.
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Affiliation(s)
- Igor V Shcherban
- Southern Federal University (SFedU), Research Center for Neurotechnology, 105/42 Bolshaya Sadovaya Str., Rostov-on-Don 344006, the Russian Federation.
| | - Victoria S Fedotova
- Southern Federal University (SFedU), Research Center for Neurotechnology, 105/42 Bolshaya Sadovaya Str., Rostov-on-Don 344006, the Russian Federation
| | - Aleksey E Matukhno
- Southern Federal University (SFedU), Research Center for Neurotechnology, 105/42 Bolshaya Sadovaya Str., Rostov-on-Don 344006, the Russian Federation
| | - Igor E Shepelev
- Southern Federal University (SFedU), Research Center for Neurotechnology, 105/42 Bolshaya Sadovaya Str., Rostov-on-Don 344006, the Russian Federation
| | - Oxana G Shcherban
- Southern Federal University (SFedU), Research Center for Neurotechnology, 105/42 Bolshaya Sadovaya Str., Rostov-on-Don 344006, the Russian Federation
| | - Larisa V Lysenko
- Southern Federal University (SFedU), Research Center for Neurotechnology, 105/42 Bolshaya Sadovaya Str., Rostov-on-Don 344006, the Russian Federation
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Huang DM, Huang J, Qiao K, Zhong NS, Lu HZ, Wang WJ. Deep learning-based lung sound analysis for intelligent stethoscope. Mil Med Res 2023; 10:44. [PMID: 37749643 PMCID: PMC10521503 DOI: 10.1186/s40779-023-00479-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .
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Affiliation(s)
- Dong-Min Huang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Jia Huang
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Kun Qiao
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Nan-Shan Zhong
- Guangzhou Institute of Respiratory Health, China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
| | - Hong-Zhou Lu
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China.
| | - Wen-Jin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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Shirani S, Valentin A, Alarcon G, Kazi F, Sanei S. Separating Inhibitory and Excitatory Responses of Epileptic Brain to Single-Pulse Electrical Stimulation. Int J Neural Syst 2023; 33:2350008. [PMID: 36495050 DOI: 10.1142/s0129065723500089] [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] [Indexed: 11/17/2022]
Abstract
To enable an accurate recognition of neuronal excitability in an epileptic brain for modeling or localization of epileptic zone, here the brain response to single-pulse electrical stimulation (SPES) has been decomposed into its constituent components using adaptive singular spectrum analysis (SSA). Given the response at neuronal level, these components are expected to be the inhibitory and excitatory components. The prime objective is to thoroughly investigate the nature of delayed responses (elicited between 100[Formula: see text]ms-1 s after SPES) for localization of the epileptic zone. SSA is a powerful subspace signal analysis method for separation of single channel signals into their constituent uncorrelated components. The consistency in the results for both early and delayed brain responses verifies the usability of the approach.
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Affiliation(s)
- Sepehr Shirani
- Department of Computer Science, School of Science and Technology, Nottingham Trent University, UK
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, UK
| | | | - Farhana Kazi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, UK
| | - Saeid Sanei
- Department of Computer Science, School of Science and Technology, Nottingham Trent University, UK
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Pinto B, Correia MV, Paredes H, Silva I. Detection of Intermittent Claudication from Smartphone Inertial Data in Community Walks Using Machine Learning Classifiers. SENSORS (BASEL, SWITZERLAND) 2023; 23:1581. [PMID: 36772621 PMCID: PMC9920000 DOI: 10.3390/s23031581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/25/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.
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Affiliation(s)
- Bruno Pinto
- INESC Technology and Science, 4200-465 Porto, Portugal
- Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
| | - Miguel Velhote Correia
- INESC Technology and Science, 4200-465 Porto, Portugal
- Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
| | - Hugo Paredes
- INESC Technology and Science, 4200-465 Porto, Portugal
- School of Science and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
| | - Ivone Silva
- Angiology and Vascular Surgery, Centro Hospitalar Universitário do Porto, 4099-001 Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, 4050-313 Porto, Portugal
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Wang W, Wang S, Qin D, Fang Y, Zheng Y. Heart-lung sound separation by nonnegative matrix factorization and deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Zhang A, Wang J, Qu F, He Z. Classification of Children's Heart Sounds With Noise Reduction Based on Variational Modal Decomposition. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:854382. [PMID: 35693881 PMCID: PMC9178247 DOI: 10.3389/fmedt.2022.854382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/15/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose Children's heart sounds were denoised to improve the performance of the intelligent diagnosis. Methods A combined noise reduction method based on variational modal decomposition (VMD) and wavelet soft threshold algorithm (WST) was proposed, and used to denoise 103 phonocardiogram samples. Features were extracted after denoising and employed for an intelligent diagnosis model to verify the effect of the denoising method. Results The noise in children's phonocardiograms, especially crying noise, was suppressed. The signal-to-noise ratio obtained by the method for normal heart sounds was 14.69 dB at 5 dB Gaussian noise, which was higher than that obtained by WST only and the other VMD denoising method. Intelligent classification showed that the accuracy, sensitivity and specificity of the classification system for congenital heart diseases were 92.23, 92.42, and 91.89%, respectively and better than those with WST only. Conclusion The proposed noise reduction method effectively eliminates noise in children's phonocardiograms and improves the performance of intelligent screening for the children with congenital heart diseases.
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Affiliation(s)
- Anqi Zhang
- Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, China
| | - Jiaming Wang
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Fei Qu
- Shanghai Lishen Information Technology Co., Ltd., Shanghai, China
| | - Zhaoming He
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, United States
- *Correspondence: Zhaoming He
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Grooby E, He J, Fattahi D, Zhou L, King A, Ramanathan A, Malhotra A, Dumont GA, Marzbanrad F. A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5668-5673. [PMID: 34892408 DOI: 10.1109/embc46164.2021.9630256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Obtaining high quality heart and lung sounds enables clinicians to accurately assess a newborns cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation based approach is proposed to separate noisy chest sound recordings into heart, lung and noise components to address this problem. This method is achieved through training with 20 high quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.
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Chen R, Xu G, Zheng Y, Yao P, Zhang S, Yan L, Zhang K. Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance. J Neural Eng 2021; 18. [PMID: 34492637 DOI: 10.1088/1741-2552/ac2459] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 09/07/2021] [Indexed: 12/21/2022]
Abstract
Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.Approach.Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.Results:A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.
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Affiliation(s)
- Ruiquan Chen
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Guanghua Xu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.,State Key Laboratory for Manufacturing systems Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Yang Zheng
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Pulin Yao
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Sicong Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Li Yan
- Guangdong Institute of Medical Instruments & National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, People's Republic of China
| | - Kai Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
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Yuan D, Jiang J, Qiao X, Qi X, Wang W. An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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10
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Ramanna S, Tirunagari S, Windridge D. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-019-00395-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:4159676. [PMID: 31976053 PMCID: PMC6955116 DOI: 10.1155/2019/4159676] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 11/29/2019] [Indexed: 12/04/2022]
Abstract
Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.
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ANTONY DHAS MM, CHANDRASEKARAN S. Particle Swarm Intelligence Based Univariate Parameter Tuning of Recursive Least Square Algorithm for Optimal Heart Sound Signal Filtering. GAZI UNIVERSITY JOURNAL OF SCIENCE 2019. [DOI: 10.35378/gujs.501114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Lin J, Walsted ES, Backer V, Hull JH, Elson DS. Quantification and Analysis of Laryngeal Closure From Endoscopic Videos. IEEE Trans Biomed Eng 2019; 66:1127-1136. [DOI: 10.1109/tbme.2018.2867636] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Safi SMM, Pooyan M, Motie Nasrabadi A. Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis (OSSA). Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.06.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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HO WENHSIEN, CHEN YENMINGJ, ZHANG YUZHEN, TAO YANYUN, KUO HSINWEN. HEART DISEASES DETECTION FROM NOISY RECORDINGS OF SMARTPHONE DEVICES. J MECH MED BIOL 2018. [DOI: 10.1142/s0219519418500392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper aims to develop an algorithm to detect heart diseases through ordinary smartphones without additional equipment for cost accessibility. Among various vital signs emitted by organs, sounds can be easily observed and carry ample information. However, these sounds are small and noisy. Detecting anomalies involves great challenges in signal processing. This study presents a novel method that overcomes noises to estimate cardiovascular health. We use time-scale techniques in time series analysis to extract disease traits and suppress excessive ambient noises. Using datasets from PhysioNet, our model achieved a nearly 100% accuracy in heart disease diagnosis. Our approach also performs well under excessive noises for diseases producing heart murmurs. With heavy noise contaminated signals, training accuracy still closed to 100%, and the testing accuracy still remained around 84%.
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Affiliation(s)
- WEN-HSIEN HO
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan, ROC
| | - YENMING J. CHEN
- Department of Logistics Management, National Kaohsiung University of Science and Technology, Taiwan, ROC
| | - YUZHEN ZHANG
- Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, P. R. China
| | - YANYUN TAO
- School of Railway Transportation, Soochow University, Suzhou, P. R. China
| | - HSIN-WEN KUO
- College of Management, National Kaohsiung University of Science and Technology, Taiwan, ROC
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Takano M, Ueno A. Noncontact In-Bed Measurements of Physiological and Behavioral Signals Using an Integrated Fabric-Sheet Sensing Scheme. IEEE J Biomed Health Inform 2018; 23:618-630. [PMID: 29994011 DOI: 10.1109/jbhi.2018.2825020] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Home monitoring requires measuring the physiological and behavioral signals without impairing a subject's everyday life. This paper presents an integrated and noncontact approach for obtaining simultaneous physiological and behavioral signals of recumbent humans in beds using a home-monitoring application. In the proposed approach, a fabric-sheet unified sensing electrode (FUSE) obtains physiological signals by recording the electrocardiogram (ECG), chest and abdominal respiratory movements (RMs), and ballistocardiogram (BCG). The FUSE also detects the behavioral signals of body proximity (BPx) and lateral/supine lying postures. A prototype system with FUSE was validated in a short-term experiment and 6-h overnight measurements on two different groups composed of seven lying subjects. The results confirmed that the approach senses each signal independently and records the ECG, RMs, BCG, and BPx signals simultaneously. The mean sensitivities of the R and T waves of the ECG during sleep were 86.1% and 88.0%, respectively, whereas those of the chest and abdominal RMs were 90.7% and 90.1%, respectively. Although our prototype system has room for improvement, the results suggest that our approach enables the unconstrained, nocturnal monitoring of the physiological and behavioral signals in recumbent humans. The at-home monitoring of the physiological and behavioral signals is expected to contribute to cost-effective personalized healthcare in the future. This noncontact and easy-to-install system for in-bed measurements can facilitate a new era of home monitoring.
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Ferdowsi S, Abolghasemi V. Multi layer spectral decomposition technique for ERD estimation in EEG μ rhythms: An EEG–fMRI study. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.10.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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TRIPATHY R, PATERNINA MARIORARRIETA, PATTANAIK P. A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.
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Affiliation(s)
- R. K. TRIPATHY
- Faculty of Engineering (ITER), S‘O’A University, Bhubaneswar 751030, India
| | - MARIO R. ARRIETA PATERNINA
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico
| | - P. PATTANAIK
- Faculty of Engineering (ITER), S‘O’A University, Bhubaneswar 751030, India
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Emmanouilidou D, McCollum ED, Park DE, Elhilali M. Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments. IEEE Trans Biomed Eng 2017. [PMID: 28641244 DOI: 10.1109/tbme.2017.2717280] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
GOAL Chest auscultations offer a non-invasive and low-cost tool for monitoring lung disease. However, they present many shortcomings, including inter-listener variability, subjectivity, and vulnerability to noise and distortions. This work proposes a computer-aided approach to process lung signals acquired in the field under adverse noisy conditions, by improving the signal quality and offering automated identification of abnormal auscultations indicative of respiratory pathologies. METHODS The developed noise-suppression scheme eliminates ambient sounds, heart sounds, sensor artifacts, and crying contamination. The improved high-quality signal is then mapped onto a rich spectrotemporal feature space before being classified using a trained support-vector machine classifier. Individual signal frame decisions are then combined using an evaluation scheme, providing an overall patient-level decision for unseen patient records. RESULTS All methods are evaluated on a large dataset with 1000 children enrolled, 1-59 months old. The noise suppression scheme is shown to significantly improve signal quality, and the classification system achieves an accuracy of 86.7% in distinguishing normal from pathological sounds, far surpassing other state-of-the-art methods. CONCLUSION Computerized lung sound processing can benefit from the enforcement of advanced noise suppression. A fairly short processing window size ( s) combined with detailed spectrotemporal features is recommended, in order to capture transient adventitious events without highlighting sharp noise occurrences. SIGNIFICANCE Unlike existing methodologies in the literature, the proposed work is not limited in scope or confined to laboratory settings: This work validates a practical method for fully automated chest sound processing applicable to realistic and noisy auscultation settings.
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Mondal A, Banerjee P, Somkuwar A. Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 139:119-136. [PMID: 28187883 DOI: 10.1016/j.cmpb.2016.10.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Revised: 09/13/2016] [Accepted: 10/24/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE There is always heart sound (HS) signal interfering during the recording of lung sound (LS) signals. This obscures the features of LS signals and creates confusion on pathological states, if any, of the lungs. In this work, a new method is proposed for reduction of heart sound interference which is based on empirical mode decomposition (EMD) technique and prediction algorithm. METHOD In this approach, first the mixed signal is split into several components in terms of intrinsic mode functions (IMFs). Thereafter, HS-included segments are localized and removed from them. The missing values of the gap thus produced, is predicted by a new Fast Fourier Transform (FFT) based prediction algorithm and the time domain LS signal is reconstructed by taking an inverse FFT of the estimated missing values. RESULTS The experiments have been conducted on simulated and recorded HS corrupted LS signals at three different flow rates and various SNR levels. The performance of the proposed method is evaluated by qualitative and quantitative analysis of the results. CONCLUSIONS It is found that the proposed method is superior to the baseline method in terms of quantitative and qualitative measurement. The developed method gives better results compared to baseline method for different SNR levels. Our method gives cross correlation index (CCI) of 0.9488, signal to deviation ratio (SDR) of 9.8262, and normalized maximum amplitude error (NMAE) of 26.94 for 0 dB SNR value.
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Affiliation(s)
- Ashok Mondal
- Department of Electronics and Communication Engineering, National Institute of Technology, Bhopal, India.
| | - Poulami Banerjee
- Department of Electronics and Communication Engineering, National Institute of Technology, Bhopal, India
| | - Ajay Somkuwar
- Department of Electronics and Communication Engineering, National Institute of Technology, Bhopal, India
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Mahvash Mohammadi S, Kouchaki S, Ghavami M, Sanei S. Improving time–frequency domain sleep EEG classification via singular spectrum analysis. J Neurosci Methods 2016; 273:96-106. [DOI: 10.1016/j.jneumeth.2016.08.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Revised: 08/10/2016] [Accepted: 08/11/2016] [Indexed: 11/28/2022]
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22
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Nersisson R, Noel MM. Heart sound and lung sound separation algorithms: a review. J Med Eng Technol 2016; 41:13-21. [DOI: 10.1080/03091902.2016.1209589] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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23
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Aydin S, Demirtaş S, Ateş K, Tunga MA. Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures. Int J Neural Syst 2016; 26:1650013. [DOI: 10.1142/s0129065716500131] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In this study, singular spectrum analysis (SSA) has been used for the first time in order to extract emotional features from well-defined electroencephalography (EEG) frequency band activities (BAs) so-called delta (0.5–4[Formula: see text]Hz), theta (4–8[Formula: see text]Hz), alpha (8–16[Formula: see text]Hz), beta (16–32[Formula: see text]Hz), gamma (32–64[Formula: see text]Hz). These five BAs were estimated by applying sixth-level multi-resolution wavelet decomposition (MRWD) with Daubechies wavelets (db-8) to single channel nonaveraged emotional EEG oscillations of 6 s for each scalp location over 16 recording sites (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, O2). Every trial was mediated by different emotional stimuli which were selected from international affective picture system (IAPS) to induce emotional states such as pleasant (P), neutral (N), and unpleasant (UP). Largest principal components (PCs) of BAs were considered as emotional features and data mining approaches were used for the first time in order to classify both three different (P, N, UP) and two contrasting (P and UP) emotional states for 30 healthy controls. Emotional features extracted from gamma BAs (GBAs) for 16 recording sites provided the high classification accuracies of 87.1% and 100% for classification of three emotional states and two contrasting emotional states, respectively. In conclusion, we found the followings: (1) Eigenspectra of high frequency BAs in EEG are highly sensitive to emotional hemispheric activations, (2) emotional states are mostly mediated by GBA, (3) pleasant pictures induce the higher cortical activation in contrast to unpleasant pictures, (4) contrasting emotions induce opposite cortical activations, (5) cognitive activities are necessary for an emotion to occur.
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Affiliation(s)
- Serap Aydin
- Biomedical Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
| | - Serdar Demirtaş
- Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey
| | - Kahraman Ateş
- Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey
| | - M. Alper Tunga
- Software Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
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Mohammadi SM, Enshaeifar S, Ghavami M, Sanei S. Classification of awake, REM, and NREM from EEG via singular spectrum analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:4769-72. [PMID: 26737360 DOI: 10.1109/embc.2015.7319460] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this study, a single-channel electroencephalography (EEG) analysis method has been proposed for automated 3-state-sleep classification to discriminate Awake, NREM (non-rapid eye movement) and REM (rapid eye movement). For this purpose, singular spectrum analysis (SSA) is applied to automatically extract four brain rhythms: delta, theta, alpha, and beta. These subbands are then used to generate the appropriate features for sleep classification using a multi class support vector machine (M-SVM). The proposed method provided 0.79 agreement between the manual and automatic scores.
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25
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Enshaeifar S, Kouchaki S, Took CC, Sanei S. Quaternion Singular Spectrum Analysis of Electroencephalogram With Application in Sleep Analysis. IEEE Trans Neural Syst Rehabil Eng 2016; 24:57-67. [PMID: 26276995 DOI: 10.1109/tnsre.2015.2465177] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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26
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Ozbek IY, Shamsi H. Heart Sound Localization in Respiratory Sound Based on a New Computationally Efficient Entropy Bound. IEEE J Biomed Health Inform 2015; 21:105-114. [PMID: 26485726 DOI: 10.1109/jbhi.2015.2491500] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A new entropy bound with low computational complexity for differential Shannon entropy estimation with kernel density approach is proposed in this study, which is based on defining a bound for the Kullback-Leibler divergence between two Gaussian mixture models. The proposed entropy bound is derived to provide computational efficiency without decreasing the accuracy in detecting heart sound segments in respiratory sound. It is shown both theoretically and experimentally that using the proposed bound in an adaptive threshold-based detection method gives very similar performance compared to that obtained by a nonparametric kernel based approach, while its computational cost is much lower. The performance of the proposed method is shown and compared with the three methods in the literature by means of experiments utilizing a database of 20 subjects. The results show that the false negative rate values for the proposed method are 1.45±1.50 % and 1.98±1.81 % for low and medium flow rates, respectively. These average values are similar to the results obtained by the alternative methods. Moreover, the average elapsed time of the proposed method for a piece of data with a length of 20 s is 0.05 s, which is significantly lower than that of the other methods.
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27
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Emmanouilidou D, McCollum ED, Park DE, Elhilali M. Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries. IEEE Trans Biomed Eng 2015; 62:2279-88. [PMID: 25879837 PMCID: PMC4568755 DOI: 10.1109/tbme.2015.2422698] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL Chest auscultation constitutes a portable low-cost tool widely used for respiratory disease detection. Though it offers a powerful means of pulmonary examination, it remains riddled with a number of issues that limit its diagnostic capability. Particularly, patient agitation (especially in children), background chatter, and other environmental noises often contaminate the auscultation, hence affecting the clarity of the lung sound itself. This paper proposes an automated multiband denoising scheme for improving the quality of auscultation signals against heavy background contaminations. METHODS The algorithm works on a simple two-microphone setup, dynamically adapts to the background noise and suppresses contaminations while successfully preserving the lung sound content. The proposed scheme is refined to offset maximal noise suppression against maintaining the integrity of the lung signal, particularly its unknown adventitious components that provide the most informative diagnostic value during lung pathology. RESULTS The algorithm is applied to digital recordings obtained in the field in a busy clinic in West Africa and evaluated using objective signal fidelity measures and perceptual listening tests performed by a panel of licensed physicians. A strong preference of the enhanced sounds is revealed. SIGNIFICANCE The strengths and benefits of the proposed method lie in the simple automated setup and its adaptive nature, both fundamental conditions for everyday clinical applicability. It can be simply extended to a real-time implementation, and integrated with lung sound acquisition protocols.
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Affiliation(s)
| | | | | | - Mounya Elhilali
- M. Elhilali is with the Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA ()
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28
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Herzig J, Bickel A, Eitan A, Intrator N. Monitoring Cardiac Stress Using Features Extracted From S1 Heart Sounds. IEEE Trans Biomed Eng 2015; 62:1169-78. [PMID: 25494499 DOI: 10.1109/tbme.2014.2377695] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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29
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Shah G, Koch P, Papadias CB. On the Blind Recovery of Cardiac and Respiratory Sounds. IEEE J Biomed Health Inform 2015; 19:151-7. [DOI: 10.1109/jbhi.2014.2349156] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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30
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Kouchaki S, Sanei S, Arbon EL, Dijk DJ. Tensor Based Singular Spectrum Analysis for Automatic Scoring of Sleep EEG. IEEE Trans Neural Syst Rehabil Eng 2015; 23:1-9. [DOI: 10.1109/tnsre.2014.2329557] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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31
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Shamsi H, Ozbek IY. A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means. Med Biol Eng Comput 2014; 53:45-56. [PMID: 25326867 DOI: 10.1007/s11517-014-1210-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 10/07/2014] [Indexed: 10/24/2022]
Abstract
This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.
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Affiliation(s)
- Hamed Shamsi
- Department of Electrical and Electronics Engineering, Atatürk University, 25240, Erzurum, Turkey,
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32
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Shamsi H, Yucel Ozbek I. Robust heart sound detection in respiratory sound using LRT with maximum a posteriori based online parameter adaptation. Med Eng Phys 2014; 36:1277-87. [DOI: 10.1016/j.medengphy.2014.07.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Revised: 05/27/2014] [Accepted: 07/02/2014] [Indexed: 12/22/2022]
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33
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Jarchi D, Wong C, Kwasnicki RM, Heller B, Tew GA, Yang GZ. Gait parameter estimation from a miniaturized ear-worn sensor using singular spectrum analysis and longest common subsequence. IEEE Trans Biomed Eng 2014; 61:1261-73. [PMID: 24658250 DOI: 10.1109/tbme.2014.2299772] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20 min on a treadmill with an increasing incline of 2% every 2 min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively.
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34
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Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features. IEEE J Biomed Health Inform 2014; 18:1138-52. [DOI: 10.1109/jbhi.2013.2294399] [Citation(s) in RCA: 116] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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35
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Azami H, Sanei S. Spike detection approaches for noisy neuronal data: Assessment and comparison. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.12.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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36
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Salehizadeh SMA, Dao DK, Chong JW, McManus D, Darling C, Mendelson Y, Chon KH. Photoplethysmograph signal reconstruction based on a novel motion artifact detection-reduction approach. Part II: Motion and noise artifact removal. Ann Biomed Eng 2014; 42:2251-63. [PMID: 24823655 DOI: 10.1007/s10439-014-1030-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 05/06/2014] [Indexed: 11/29/2022]
Abstract
We introduce a new method to reconstruct motion and noise artifact (MNA) contaminated photoplethysmogram (PPG) data. A method to detect MNA corrupted data is provided in a companion paper. Our reconstruction algorithm is based on an iterative motion artifact removal (IMAR) approach, which utilizes the singular spectral analysis algorithm to remove MNA artifacts so that the most accurate estimates of uncorrupted heart rates (HRs) and arterial oxygen saturation (SpO2) values recorded by a pulse oximeter can be derived. Using both computer simulations and three different experimental data sets, we show that the proposed IMAR approach can reliably reconstruct MNA corrupted data segments, as the estimated HR and SpO2 values do not significantly deviate from the uncorrupted reference measurements. Comparison of the accuracy of reconstruction of the MNA corrupted data segments between our IMAR approach and the time-domain independent component analysis (TD-ICA) is made for all data sets as the latter method has been shown to provide good performance. For simulated data, there were no significant differences in the reconstructed HR and SpO2 values starting from 10 dB down to -15 dB for both white and colored noise contaminated PPG data using IMAR; for TD-ICA, significant differences were observed starting at 10 dB. Two experimental PPG data sets were created with contrived MNA by having subjects perform random forehead and rapid side-to-side finger movements show that; the performance of the IMAR approach on these data sets was quite accurate as non-significant differences in the reconstructed HR and SpO2 were found compared to non-contaminated reference values, in most subjects. In comparison, the accuracy of the TD-ICA was poor as there were significant differences in reconstructed HR and SpO2 values in most subjects. For non-contrived MNA corrupted PPG data, which were collected with subjects performing walking and stair climbing tasks, the IMAR significantly outperformed TD-ICA as the former method provided HR and SpO2 values that were non-significantly different than MNA free reference values.
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Affiliation(s)
- S M A Salehizadeh
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609-2280, USA,
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37
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Zivanovic M, González-Izal M. Quasi-periodic modeling for heart sound localization and suppression in lung sounds. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.06.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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38
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Mondal A, Bhattacharya P, Saha G. An automated tool for localization of heart sound components S1, S2, S3 and S4 in pulmonary sounds using Hilbert transform and Heron's formula. SPRINGERPLUS 2013; 2:512. [PMID: 24255827 PMCID: PMC3825056 DOI: 10.1186/2193-1801-2-512] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Accepted: 09/27/2013] [Indexed: 11/10/2022]
Abstract
ABSTRACT The primary problem with lung sound (LS) analysis is the interference of heart sound (HS) which tends to mask important LS features. The effect of heart sound is more at medium and high flow rate than that of low flow rate. Moreover, pathological HS obscures LS in a higher degree than normal HS. To get over this problem, several HS reduction techniques have been developed. An important preprocessing step in HS reduction is localization of HS components. In this paper, a new HS localization algorithm is proposed which is based on Hilbert transform (HT) and Heron's formula. In the proposed method, the HS included segment is differentiated from the HS excluded segment by comparing their area with an adaptive threshold. The area of a HS component is calculated from the Hilbert envelope using Heron's triangular formula. The method is tested on real recorded and simulated HS corrupted LS signals. All the experiments are conducted under low, medium and high breathing flow rates. The proposed method shows a better performance than the comparative Singular Spectrum Analysis (SSA) based method in terms of accuracy (ACC), detection error rate (DER), false negative rate (FNR), and execution time (ET).
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Affiliation(s)
- Ashok Mondal
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, 721 302 India
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Shamsi H, Ozbek IY. Heart sound localization in chest sound using temporal fuzzy C-means classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:5286-9. [PMID: 23367122 DOI: 10.1109/embc.2012.6347187] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Most of heart sound cancellation algorithms to improve the quality of lung sound use information about heart sound locations. Therefore, a reliable estimation of heart sound localizations within chest sound is a key issue to enhance the performance of heart sound cancellation algorithms. In this paper, we present a new technique to estimate locations of heart sound segments in chest sound using the temporal fuzzy c-means (TFCM) algorithm. In applying the method, chest sound is first divided into frames and then for each frame, the entropy feature is calculated. Next, by means of these features, the TFCM algorithm is applied to classify a chest sound into two classes: heart sound (heart sound containing lung sound) and non-heart sound (only lung sound). The proposed method was tested on the database used in the liteature and experimetal results are compared with the baseline which is a well-known method in the literature. The experimental results show that the proposed method outperforms the baseline method interms of false negative rate (FNR), false positive rate (FPR) and accuracy (ACC).
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Affiliation(s)
- Hamed Shamsi
- Department of Electrical and Electronics Engineering, Ataturk University, Turkey.
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40
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Çağlar F, Ozbek IY. Online estimation of lower and upper bounds for heart sound boundaries in chest sound using Convex-hull algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:4254-4257. [PMID: 23366867 DOI: 10.1109/embc.2012.6346906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Heart sound localization in chest sound is an essential part for many heart sound cancellation algorithms. The main difficulty for heart sound localization methods is the precise determination of the onset and offset boundaries of the heart sound segment. This paper presents a novel method to estimate lower and upper bounds for the onset and offset of the heart sound segment, which can be used as anchor points for more precise estimation. For this purpose, first chest sound is divided into frames and then entropy and smoothed entropy features of these frames are extracted, and used in the Convex-hull algorithm to estimate the upper and lower bounds for heart sound boundaries. The Convex-hull algorithm constructs a special type of envelope function for entropy features and if the maximal difference between the envelope function and the entropy is larger than a certain threshold, this point is considered as a heart sound bound. The results of the proposed method are compared with a baseline method which is a modified version of a well-known heart sound localization method. The results show that the proposed method outperforms the baseline method in terms of accuracy and detection error rate. Also, the experimental results show that smoothing entropy features significantly improves the performance of both baseline and proposed methods.
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Affiliation(s)
- F Çağlar
- Tercan Vocational School, Erzincan University, Erzincan, Turkey.
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