Determination of optimal heart rate variability features based on SVM-recursive feature elimination for cumulative stress monitoring using ECG sensor

Dajeong Park, Miran Lee, Sunghee E. Park, Jun Kyung Seong, Inchan Youn

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Article number2387
JournalSensors (Switzerland)
Volume18
Issue number7
DOIs
Publication statusPublished - 2018 Jul 23

Fingerprint

heart rate
Electrocardiography
elimination
Heart Rate
Monitoring
sensors
Sensors
Delivery of Health Care
intervals
Heart Rate Determination
Rodentia
kernel functions
rodents
Support vector machines
classifiers
Classifiers
systems engineering
Systems analysis
Health
health

Keywords

  • Cumulative stress
  • Electrocardiogram
  • Heart rate variability
  • Stress monitoring
  • Support vector machine-recursive feature elimination

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Determination of optimal heart rate variability features based on SVM-recursive feature elimination for cumulative stress monitoring using ECG sensor. / Park, Dajeong; Lee, Miran; Park, Sunghee E.; Seong, Jun Kyung; Youn, Inchan.

In: Sensors (Switzerland), Vol. 18, No. 7, 2387, 23.07.2018.

Research output: Contribution to journalArticle

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