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Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of ECG signals: The case of intercity roads.

Authors
  • Rostamzadeh, Sajjad1
  • Abouhossein, Alireza2
  • Vosoughi, Shahram3
  • Gendeshmin, Saeid Bahramzadeh4
  • Yarahmadi, Rasoul5
  • 1 Occupational Health Research Center, Iran University of Medical Sciences, Tehran, Iran. , (Iran)
  • 2 Department of Ergonomics, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. , (Iran)
  • 3 Occupational Health Research Center, Department of Occupational Health, School of Public Health, Iran University of Medical Sciences, Tehran, Iran. , (Iran)
  • 4 Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. , (Iran)
  • 5 Department of Occupational Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran. , (Iran)
Type
Published Article
Journal
International journal of occupational safety and ergonomics : JOSE
Publication Date
Dec 11, 2023
Pages
1–35
Identifiers
DOI: 10.1080/10803548.2023.2293391
PMID: 38083847
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

This study examines which one of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. This cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22-67 years. Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We have considered the HRV and MV metrics of ECG signals including mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), a difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). The analysis showed that HRV metrics named mHR, mRR, nVLF, nLF, nHF, dLFHF, and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, approximately 92%. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver's state.

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