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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A deep learning-based sleep staging model was tested in MSLT (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009143) and MWT (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009127) 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\u2013wake classification and mean sleep latency (MSL).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0002) and in MWT (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0012) 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10439-026-04393-2","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:22:15Z","timestamp":1790605335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Assessment of Sleepiness from Fingertip Pulse Wave Analysis During Objective Daytime Testing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9633-6016","authenticated-orcid":false,"given":"Matias","family":"Rusanen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samu","family":"Kainulainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Myllymaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timo","family":"Lepp\u00e4nen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renaud","family":"Tamisier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9bastien","family":"Baillieul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9bastien","family":"Bailly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Louis","family":"Pepin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"4393_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(22)01018-2","author":"L P\u00e9rez-Carbonell","year":"2022","unstructured":"P\u00e9rez-Carbonell, L., E. 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