Tracking vigilance fluctuations in real-time: a sliding-window heart rate variability-based machine-learning approach.

Journal: Sleep
PMID: 39185558

Abstract

STUDY OBJECTIVES: Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. This study aimed to improve the objectivity and efficiency of HRV-based vigilance evaluation by associating HRV and behavior metrics through a sliding window approach.

Authors

  • Tian Xie
    Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.
  • Ning Ma
    Key Laboratory of Preparation and Applications of Environmental Friendly Materials (Jilin Normal University), Ministry of Education, Changchun 130103, PR China.

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