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

Sliding-window-based HRV metrics would effectively capture the fluctuations in vigilance during task execution, enabling more timely and accurate detection of performance decrement.

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Publication Details

Journal
SLEEP
Published
2024-08-26
DOI
https://doi.org/10.1093/sleep/zsae199
Citations
10
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
5.08
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article

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

Ning Ma, Tian Xie
10 citations
SLEEP
Sleep and Work-Related Fatigue
5.08
article

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

Ning Ma, Tian Xie
article en
10 citations

Abstract

Sliding-window-based HRV metrics would effectively capture the fluctuations in vigilance during task execution, enabling more timely and accurate detection of performance decrement.

SLEEPVol. 48(2)
South China Normal University (CN)
Openalex Percentile: Top 4%
Sleep and Work-Related Fatigue
5.08
DOISourceOpenAlex
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Tracking vigilance fluctuations in real-time: a sliding-window heart rate variability-based machine-learning approach — Ning Ma, Tian Xie · SLEEP (2024) | TGRS Research Map | TGRS