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Sensors (Basel). 2018 Jul 23;18(7). pii: E2387. doi: 10.3390/s18072387.

Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor.

Author information

1
Biomedical Research Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea. dajeong@kist.re.kr.
2
Biomedical Research Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea. leemiran7461@gmail.com.
3
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA. park.estelles@gmail.com.
4
Department of Bio-convergence Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea. jkseong@korea.ac.kr.
5
Biomedical Research Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea. iyoun@kist.re.kr.
6
Department of KHU-KIST Converging Science and Technology, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea. iyoun@kist.re.kr.

Abstract

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.

KEYWORDS:

cumulative stress; electrocardiogram; heart rate variability; stress monitoring; support vector machine-recursive feature elimination

PMID:
30041417
PMCID:
PMC6069263
DOI:
10.3390/s18072387
[Indexed for MEDLINE]
Free PMC Article

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