Derivative method to detect sleep and awake states through heart rate variability analysis using machine learning algorithms

Auteurs

Fabrice Vaussenat, Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, H3C 1K3, QC, Canada
Abhiroop Bhattacharya, Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, H3C 1K3, QC, Canada
Philippe Boudreau, Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, H4H 1R3, QC, Canada
Diane B. Boivin, Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, H4H 1R3, QC, Canada
Ghyslain Gagnon, Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, H3C 1K3, QC, Canada
Sylvain G. Cloutier, Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, H3C 1K3, QC, Canada

Type de document

Études primaires

Année de publication

2024

Langue

Anglais

Titre de la revue

Sensors

Résumé

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping. © 2024 by the authors.

Mots-clés

Perturbation du sommeil, Sleep disturbance, Intelligence artificielle, Artificial intelligence, Essai du matériel, Equipment testing, Analyse des données, Data analysis

Numéro de projet IRSST

2020-0006

Citation recommandée

Vaussenat, F., Bhattacharya, A., Boudreau, P., Boivin, D. B., Gagnon, G. et Cloutier, S. G. (2024). Derivative method to detect sleep and awake states through heart rate variability analysis using machine learning algorithms. Sensors, 24(13). https://doi.org/10.3390/s24134317