Tracking Vigilance Fluctuations in Real-Time: A Sliding-Window HRV-based Machine-Learning Approach.
Sleep
; 2024 Aug 26.
Article
in En
| MEDLINE
| ID: mdl-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.METHODS:
Forty-four healthy adults underwent psychomotor vigilance tasks under both well-rested and sleep-deprived conditions, with simultaneous electrocardiogram recording. A sliding-window approach (30s length, 10s step) was used for HRV feature extraction and behavior assessment. Repeated-measures ANOVA was used to examine how HRV related to objective vigilance levels. Stability selection technique was applied for feature selection, and the vigilance ground truth-high (fastest 40%), intermediate (middle 20%), and low (slowest 40%)-were determined based on each participant's range of performance. Four machine-learning classifiers-k-nearest neighbours, support vector machine (SVM), AdaBoost, and random forest-were trained and tested using cross-validation.RESULTS:
Fluctuated vigilance performance indicated pronounced state instability, particularly after sleep deprivation. Temporary decrements in performance were associated with a decrease in heart rate and an increase in time-domain heart rate variability. SVM achieved the best performance, with a cross-validated accuracy of 89% for binary classification of high versus low vigilance epochs. Overall accuracy dropped to 72% for three-class classification in leave-one-participant-out cross-validation, but SVM maintained a precision of 84% in identifying low-vigilance epochs.CONCLUSIONS:
Sliding-window-based HRV metrics would effectively capture the fluctuations in vigilance during task execution, enabling more timely and accurate detection of performance decrement.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Sleep
Year:
2024
Document type:
Article
Affiliation country:
China