Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization
With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovas...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7605 |
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| author | Te-Jen Su Ya-Chung Hung Tzung-Shiarn Pan Wei-Hong Lin Shih-Ming Wang Yu-Cheng Lee |
| author_facet | Te-Jen Su Ya-Chung Hung Tzung-Shiarn Pan Wei-Hong Lin Shih-Ming Wang Yu-Cheng Lee |
| author_sort | Te-Jen Su |
| collection | DOAJ |
| description | With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers. |
| first_indexed | 2024-03-11T01:46:53Z |
| format | Article |
| id | doaj.art-345639272fad4d54b6c1e2503aab2ac5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:53Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-345639272fad4d54b6c1e2503aab2ac52023-11-18T16:08:48ZengMDPI AGApplied Sciences2076-34172023-06-011313760510.3390/app13137605Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm OptimizationTe-Jen Su0Ya-Chung Hung1Tzung-Shiarn Pan2Wei-Hong Lin3Shih-Ming Wang4Yu-Cheng Lee5Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 80782, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 80782, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 80782, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 80782, TaiwanDepartment of Computer Science and Information Engineering, Cheng Shiu University, Kaohsiung 833, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 80782, TaiwanWith the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers.https://www.mdpi.com/2076-3417/13/13/7605independent component analysisheart rate variabilityheart rateparticle swarm optimization algorithm |
| spellingShingle | Te-Jen Su Ya-Chung Hung Tzung-Shiarn Pan Wei-Hong Lin Shih-Ming Wang Yu-Cheng Lee Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization Applied Sciences independent component analysis heart rate variability heart rate particle swarm optimization algorithm |
| title | Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization |
| title_full | Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization |
| title_fullStr | Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization |
| title_full_unstemmed | Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization |
| title_short | Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization |
| title_sort | estimation of heart rate and heart rate variability with real time images based on independent component analysis and particle swarm optimization |
| topic | independent component analysis heart rate variability heart rate particle swarm optimization algorithm |
| url | https://www.mdpi.com/2076-3417/13/13/7605 |
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