Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography
Abstract
BACKGROUND: Traditional sleep metrics from polysomnography are often limited by the hypnogram, in which each 30-s epoch is classified into one of the five sleep stages. This oversimplifies sleep stage continuity and fragmented sleep, typical in sleep-disordered breathing (SDB). Deep learning offers more dynamic sleep modelling with a higher temporal resolution by providing probabilities for each sleep stage, known as hypnodensities. RESEARCH QUESTION AND METHODS: We investigated whether high-frequency (1/s) hypnodensities captured from nocturnal fingertip photoplethysmography (PPG) recordings better correlate with patient-reported outcomes in SDB than traditional metrics. RESULTS: Among 2,280 patients (65% male, mean age 62 years, mean apnoea-hypopnoea index (AHI) 23.5/h), we found that several PPG-derived hypnodensity metrics correlate with self-reported daytime fatigue and sleepiness. The level of correlation was better or similar compared to traditional hypnogram-based sleep metrics derived from manually scored polysomnography. Specifically, PPG-based hypnodensities for N1 and N3 sleep showed a stronger association (p < 0.005) with fatigue and sleepiness than manually scored N1 and N3 sleep. Multivariable logistic regression (adjusted for age, sex, body mass index, AHI, and total sleep time below 90% blood oxygen saturation) revealed significant associations (p < 0.01) between multiple PPG-based hypnodensity metrics and daytime sleepiness. CONCLUSION: These findings suggest that PPG-based hypnodensities improve the characterisation of sleep quality and better predict patient-reported outcomes in SDB patients. Future uses may include tracking sleep quality with fingertip pulse oximeters outside of sleep centres, potentially over multiple nights due to their simplicity.
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BibTeX
@article{rusanen2026hypnodensity,
title = {{Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography}},
author = {Matias Rusanen and Viateur Tuyinsenge and Sandrine Muller and Renaud Tamisier and Sébastien Baillieul and Sami Myllymaa and Samu Kainulainen and Timo Leppänen and Jean-Louis Pepin and Sébastien Bailly},
journal = {Pulmonology},
year = {2026},
month = jun,
doi = {10.1080/25310429.2026.2683326},
url = {https://hal.science/hal-05744213v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography
AU - Matias Rusanen
AU - Viateur Tuyinsenge
AU - Sandrine Muller
AU - Renaud Tamisier
AU - Sébastien Baillieul
AU - Sami Myllymaa
AU - Samu Kainulainen
AU - Timo Leppänen
AU - Jean-Louis Pepin
AU - Sébastien Bailly
PY - 2026
DA - 2026/06/12
JO - Pulmonology
DO - 10.1080/25310429.2026.2683326
UR - https://hal.science/hal-05744213v1
AB - BACKGROUND: Traditional sleep metrics from polysomnography are often limited by the hypnogram, in which each 30-s epoch is classified into one of the five sleep stages. This oversimplifies sleep stage continuity and fragmented sleep, typical in sleep-disordered breathing (SDB). Deep learning offers more dynamic sleep modelling with a higher temporal resolution by providing probabilities for each sleep stage, known as hypnodensities. RESEARCH QUESTION AND METHODS: We investigated whether high-frequency (1/s) hypnodensities captured from nocturnal fingertip photoplethysmography (PPG) recordings better correlate with patient-reported outcomes in SDB than traditional metrics. RESULTS: Among 2,280 patients (65% male, mean age 62 years, mean apnoea-hypopnoea index (AHI) 23.5/h), we found that several PPG-derived hypnodensity metrics correlate with self-reported daytime fatigue and sleepiness. The level of correlation was better or similar compared to traditional hypnogram-based sleep metrics derived from manually scored polysomnography. Specifically, PPG-based hypnodensities for N1 and N3 sleep showed a stronger association (p < 0.005) with fatigue and sleepiness than manually scored N1 and N3 sleep. Multivariable logistic regression (adjusted for age, sex, body mass index, AHI, and total sleep time below 90% blood oxygen saturation) revealed significant associations (p < 0.01) between multiple PPG-based hypnodensity metrics and daytime sleepiness. CONCLUSION: These findings suggest that PPG-based hypnodensities improve the characterisation of sleep quality and better predict patient-reported outcomes in SDB patients. Future uses may include tracking sleep quality with fingertip pulse oximeters outside of sleep centres, potentially over multiple nights due to their simplicity.
SN - 2531-0429
SN - 2531-0437
ER -