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Deep Learning-Based Assessment of Sleepiness from Fingertip Pulse Wave Analysis During Objective Daytime Testing
Matias Rusanen1,2,3,4, Samu Kainulainen5,6,7, Sami Myllymaa5,6
1HP2 Laboratory, INSERM U1300, Grenoble Alpes University, Grenoble Alpes University Hospital, Grenoble, France. matias.rusanen@uef.fi.
Purpose:
Objective evaluation of daytime sleepiness is a key factor in the management of sleep disorders. However, objective testing is limited due to the labor-intensive nature of standard methods; the Multiple Sleep Latency Test (MSLT) and the Maintenance of Wakefulness Test (MWT). Deep learning analysis of a simple fingertip pulse wave (photoplethysmography, PPG) recording could be a viable option for the routine assessment of sleepiness in patients with suspected sleep disorders.
Methods:
A deep learning-based sleep staging model was tested in MSLT (n = 143) and MWT (n = 127) datasets. The primary aim was to analyze the classification of subjects as sleepy versus non-sleepy using PPG-based sleep analysis. As an intermediate technical evaluation, we assessed PPG-based sleep-wake classification and mean sleep latency (MSL).
Results:
Classification of sleepiness status based on automatic MSL detection from PPG showed promising results in both MSLT (accuracy: 80%, sensitivity: 65%, specificity: 84%) and MWT (accuracy: 83%, sensitivity: 62%, specificity: 86%). Sleep probability curves estimated from the fingertip pulse wave differed significantly between sleepy and non-sleepy groups, both in MSLT (p = 0.0002) and in MWT (p = 0.0012) datasets. In MSLT, the model detected sleep with moderate agreement and balanced performance (accuracy 81%, precision 0.71, recall 0.80). In MWT, overall accuracy (88%) was strongly influenced by class imbalance (98% wakefulness), and direct second-by-second sleep detection showed limited precision (precision 0.10, recall 0.72). Automated MSL estimates showed correlation with manual scoring in both MSLT and MWT; however, larger errors and outliers were observed in MWT.
Conclusion:
Fingertip pulse wave analysis combined with deep learning shows feasibility for automated assessment of daytime sleepiness, particularly in MSLT. However, direct zero-shot application of a model trained on overnight PSG to MWT is limited by low sleep precision requiring methodological adaptation and daytime-specific optimization.
