Voice is a reservoir of valuable health data. Recent studies highlighted its efficacy in predicting sleep quality, and its potential as biomarker of neurodegeneration. This study assesses the feasibility of a Telemedicine system for the evaluation of sleep quality through brief vocal recordings. Machine Learning models were employed in the binary classification between good and poor sleepers, with great performance in scoring poor sleep quality - 88% and 85% F-1 score on a 5-fold Cross Validation (CV) for females and males, respectively. Moreover, the correlation between perceived sleep quality and a validated global score was studied, as well as the influence of external factors and sleep-wake schedule.

Sleep Quality through Vocal Analysis: a Telemedicine Application / Amato, F.; Rechichi, I.; Borzi', L.; Olmo, G.. - ELETTRONICO. - (2022), pp. 706-711. ((Intervento presentato al convegno 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022 tenutosi a Pisa (ITA) nel 2022 [10.1109/PerComWorkshops53856.2022.9767372].

Sleep Quality through Vocal Analysis: a Telemedicine Application

Amato F.;Rechichi I.;Borzi' L.;Olmo G.
2022

Abstract

Voice is a reservoir of valuable health data. Recent studies highlighted its efficacy in predicting sleep quality, and its potential as biomarker of neurodegeneration. This study assesses the feasibility of a Telemedicine system for the evaluation of sleep quality through brief vocal recordings. Machine Learning models were employed in the binary classification between good and poor sleepers, with great performance in scoring poor sleep quality - 88% and 85% F-1 score on a 5-fold Cross Validation (CV) for females and males, respectively. Moreover, the correlation between perceived sleep quality and a validated global score was studied, as well as the influence of external factors and sleep-wake schedule.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2968812