Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor
Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective mon...
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MDPI AG
2018-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/7/2387 |
| _version_ | 1828150648077549568 |
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| author | Dajeong Park Miran Lee Sunghee E. Park Joon-Kyung Seong Inchan Youn |
| author_facet | Dajeong Park Miran Lee Sunghee E. Park Joon-Kyung Seong Inchan Youn |
| author_sort | Dajeong Park |
| collection | DOAJ |
| description | Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life. |
| first_indexed | 2024-04-11T21:46:16Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:46:16Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e10f16b7231f4c54989b33909c804c5e2022-12-22T04:01:25ZengMDPI AGSensors1424-82202018-07-01187238710.3390/s18072387s18072387Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG SensorDajeong Park0Miran Lee1Sunghee E. Park2Joon-Kyung Seong3Inchan Youn4Biomedical Research Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaBiomedical Research Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Bio-convergence Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, KoreaBiomedical Research Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaRoutine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life.http://www.mdpi.com/1424-8220/18/7/2387heart rate variabilitycumulative stresselectrocardiogramstress monitoringsupport vector machine-recursive feature elimination |
| spellingShingle | Dajeong Park Miran Lee Sunghee E. Park Joon-Kyung Seong Inchan Youn Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor Sensors heart rate variability cumulative stress electrocardiogram stress monitoring support vector machine-recursive feature elimination |
| title | Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor |
| title_full | Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor |
| title_fullStr | Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor |
| title_full_unstemmed | Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor |
| title_short | Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor |
| title_sort | determination of optimal heart rate variability features based on svm recursive feature elimination for cumulative stress monitoring using ecg sensor |
| topic | heart rate variability cumulative stress electrocardiogram stress monitoring support vector machine-recursive feature elimination |
| url | http://www.mdpi.com/1424-8220/18/7/2387 |
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