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Article

Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature

by
Andrea Di Credico
1,2,*,†,
David Perpetuini
3,†,
Pascal Izzicupo
1,
Giulia Gaggi
1,2,
Nicola Mammarella
4,
Alberto Di Domenico
4,
Rocco Palumbo
4,
Pasquale La Malva
4,
Daniela Cardone
3,
Arcangelo Merla
2,3,
Barbara Ghinassi
1,2 and
Angela Di Baldassarre
1,2
1
Department of Medicine and Aging Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy
2
UdA-TechLab, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy
3
Department of Engineering and Geology, “G. D’Annunzio” University of Chieti-Pescara, 65127 Pescara, Italy
4
Department of Psychological, Health and Territorial Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Clocks & Sleep 2024, 6(3), 322-337; https://doi.org/10.3390/clockssleep6030023
Submission received: 20 May 2024 / Revised: 17 July 2024 / Accepted: 19 July 2024 / Published: 23 July 2024
(This article belongs to the Section Computational Models)
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Abstract

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.
Keywords: sleep quality; wearable sensors; contactless sensors; heart rate variability; skin temperature; infrared thermography; machine learning sleep quality; wearable sensors; contactless sensors; heart rate variability; skin temperature; infrared thermography; machine learning

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MDPI and ACS Style

Di Credico, A.; Perpetuini, D.; Izzicupo, P.; Gaggi, G.; Mammarella, N.; Di Domenico, A.; Palumbo, R.; La Malva, P.; Cardone, D.; Merla, A.; et al. Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature. Clocks & Sleep 2024, 6, 322-337. https://doi.org/10.3390/clockssleep6030023

AMA Style

Di Credico A, Perpetuini D, Izzicupo P, Gaggi G, Mammarella N, Di Domenico A, Palumbo R, La Malva P, Cardone D, Merla A, et al. Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature. Clocks & Sleep. 2024; 6(3):322-337. https://doi.org/10.3390/clockssleep6030023

Chicago/Turabian Style

Di Credico, Andrea, David Perpetuini, Pascal Izzicupo, Giulia Gaggi, Nicola Mammarella, Alberto Di Domenico, Rocco Palumbo, Pasquale La Malva, Daniela Cardone, Arcangelo Merla, and et al. 2024. "Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature" Clocks & Sleep 6, no. 3: 322-337. https://doi.org/10.3390/clockssleep6030023

APA Style

Di Credico, A., Perpetuini, D., Izzicupo, P., Gaggi, G., Mammarella, N., Di Domenico, A., Palumbo, R., La Malva, P., Cardone, D., Merla, A., Ghinassi, B., & Di Baldassarre, A. (2024). Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature. Clocks & Sleep, 6(3), 322-337. https://doi.org/10.3390/clockssleep6030023

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