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Chou Y, Yang M, Sun Y, Chou L, Zhou Y, An A. Malignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal. Med Eng Phys 2024; 123:104085. [PMID: 38365338 DOI: 10.1016/j.medengphy.2023.104085] [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: 04/06/2023] [Revised: 09/05/2023] [Accepted: 12/10/2023] [Indexed: 02/18/2024]
Abstract
Extreme bradycardia, extreme tachycardia, ventricular flutter fib, and ventricular tachycardia are four malignant arrhythmias (MAs) that lead to sudden cardiac death. It is very important to detect them in daily life. The arterial blood pressure (ABP) signal contains abundant pathological information about four MAs and is easy to be recorded under domestic conditions. Thus, a synthesis-by-analysis (SA) modeling method for ABP signal was proposed to detect the four MAs in this study. The average models of MAs and healthy subjects were obtained by SA modeling, and the change of each ABP wave was quantitively described by twelve parameters of wave models. Then, the probabilistic neural network (PNN) and random forest (RF) are trained to detect the MAs. The experimental data were employed from Fantasia and the 2015 PhysioNet/CinC Challenge databases. The SA modeling results show that some pathological and physiological changes could be extracted from the average models. The two-sample ks-test results between different groups are markedly different (h = 1, p < 0.05). The detection results show that the performances of PPN classifiers are less than that of RF. The kappa coefficients (KC) for the RF classifiers are 97.167 ± 1.46 %, 97.888 ± 0.808 %, 99.895 ± 0.545 %, 98.575 ± 1.683 % and 92.241 ± 1.517 %, respectively. The mean KC is 97.083 ± 0.67 %. Compared to the performance of some existing studies, the proposed method has better performance and is potential to diagnose MAs in m-health.
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Affiliation(s)
- Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Miao Yang
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China; College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Yiyun Sun
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Yan Zhou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China; College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Aimin An
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
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Yao A, Chou Y, Yang L, Hu L, Liu J, Gu S. Research on heart rate extraction method based on mobile phone video. Med Eng Phys 2023; 120:104051. [PMID: 37838408 DOI: 10.1016/j.medengphy.2023.104051] [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: 04/05/2023] [Revised: 07/26/2023] [Accepted: 09/11/2023] [Indexed: 10/16/2023]
Abstract
As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy. To overcome these problems, this study proposed a novel heart rate extraction method based on mobile video. Firstly, the mobile phone camera is engaged to record the finger video, the region of interest (ROI) is extracted through the iterative threshold, and the pulse signal is obtained according to the grayscale change of the resolution within the ROI. Then, a low-pass and a high-pass Butterworth filters are exploited to filter out the noise and interframes from the extracted pulse signal. Finally, an improved adaptive peak extraction algorithm is proposed to detect the pulse peaks and the heart rate derived from the difference in pulse peaks. The experimental results show that light intensity, frame rate and resolution all have an influence on the heart rate extraction accuracy, with the most obvious influence of light, the average accuracy of the experiment can reach 99.32 % under good lighting conditions, while only 72.23 % under poor lighting conditions. In terms of frame rate, increasing the frame rate from 30 fps to 60 fps, the accuracy is improved by 0.9 %. For the resolution, increasing the resolution from 1080 p to 2160 p, the accuracy is improved by 1.12 %. While comparing the proposed method with existing methods, the proposed method has a higher accuracy rate, which has important practical value and application prospects in telemedicine and daily monitoring.
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Affiliation(s)
- An Yao
- School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224000, China; School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China.
| | - Liming Yang
- School of Internet of Things Application Technology, Changzhou College of Information Technology, Changzhou 213164, China
| | - Linqi Hu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China; School of Chemical Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Suhang Gu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China
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Chou L, Gong S, Yang H, Liu J, Chou Y. A fast sample entropy for pulse rate variability analysis. Med Biol Eng Comput 2023:10.1007/s11517-022-02766-y. [PMID: 36826631 DOI: 10.1007/s11517-022-02766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 12/22/2022] [Indexed: 02/25/2023]
Abstract
Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.
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Affiliation(s)
- Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
| | - Shengrong Gong
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
- School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
| | - Haiping Yang
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China.
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Chou L, Liu J, Gong S, Chou Y. A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree. Front Physiol 2022; 13:1008111. [PMID: 36311226 PMCID: PMC9614148 DOI: 10.3389/fphys.2022.1008111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/23/2022] [Indexed: 01/11/2023] Open
Abstract
Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.
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Affiliation(s)
- Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,School of Computer and Information Technology, Northeast Petroleum University, Daqing, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China
| | - Shengrong Gong
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, China,School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,*Correspondence: Yongxin Chou,
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Behar JA, Liu C, Zigel Y, Laguna P, Clifford GD. Editorial on Remote Health Monitoring: from chronic diseases to pandemics. Physiol Meas 2021; 41:100401. [PMID: 33393486 DOI: 10.1088/1361-6579/abbb6d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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