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Meas."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Wrist-worn photoplethysmography (PPG) enables scalable, long-term unobtrusive sleep monitoring through the expression of sympathetic and parasympathetic activity in heart rate variability. However, interindividual differences in the sympatho-vagal balance may inherently limited general PPG-based sleep staging models. This study investigates whether learning individual autonomic representations through model personalization can improve PPG-based automated sleep staging performance. <jats:italic>Approach.<\/jats:italic> Concurrent wrist-worn PPG and wearable electroencephalography (EEG) were collected during home monitoring for up to seven nights in a heterogeneous sleep-disordered population (<jats:italic>n<\/jats:italic> = 59). Personalization was performed through finetuning (i.e. partial retraining) a general PPG-based model by coupling the subject-specific PPG data with the wearable EEG stage classifications. Performance of the general and personalized models were compared on PPG acquired during a gold-standard clinical polysomnography, testing their agreement on 4-stage classification (W\/N1+N2\/N3\/REM) with the manual scoring. <jats:italic>Main result.<\/jats:italic> Overall performance increased in 82.5% of the subjects, with significantly improved performance reached when personalizing the model on three or more training nights. Performance increased with personalization on additional training nights for each stage: wake (<jats:italic>\u03b2<\/jats:italic>= .005, <jats:italic>p<\/jats:italic> &lt; .001), N1+N2 (<jats:italic>\u03b2<\/jats:italic> = .003, <jats:italic>p<\/jats:italic> &lt; .001), N3 (<jats:italic>\u03b2<\/jats:italic> = .004, <jats:italic>p<\/jats:italic> &lt; .001), and REM (<jats:italic>\u03b2<\/jats:italic> = .005, <jats:italic>p<\/jats:italic> &lt; .001). Effects were strongest for younger individuals (<jats:italic>\u03b2<\/jats:italic> = .009, <jats:italic>p<\/jats:italic> &lt; .001) and patients with insomnia (<jats:italic>\u03b2<\/jats:italic> = .011, <jats:italic>p<\/jats:italic> &lt; .001). Personalization greatly impacted the derived sleep macrostructural sleep parameters, with considerable improvement in N3 sleep classification, and in capturing rapid eye movement (REM) sleep fragmentation. <jats:italic>Significance.<\/jats:italic> Personalization can overcome one-size-fits-all limitations of a general model and should be considered for PPG-based sleep staging when an altered autonomic modulation is expected that deviates from the general model\u2019s global representation.<\/jats:p>","DOI":"10.1088\/1361-6579\/ae0119","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T22:53:43Z","timestamp":1756508023000},"page":"095012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning individual autonomic representations of sleep stages to improve photoplethysmography based sleep monitoring"],"prefix":"10.1088","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6387-5144","authenticated-orcid":true,"given":"Jaap F","family":"van der Aar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2138-5686","authenticated-orcid":false,"given":"Merel M","family":"van Gilst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daan A","family":"van den Ende","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastiaan","family":"Overeem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1231-9372","authenticated-orcid":true,"given":"Elisabetta","family":"Peri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2932-6402","authenticated-orcid":true,"given":"Pedro","family":"Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"pmeaae0119bib1","doi-asserted-by":"publisher","first-page":"zsac154","DOI":"10.1093\/sleep\/zsac154","type":"journal-article","article-title":"Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring","volume":"46","author":"Bakker","year":"2023","journal-title":"Sleep"},{"key":"pmeaae0119bib2","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/JBHI.2020.3037693","type":"journal-article","article-title":"MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning","volume":"25","author":"Banluesombatkul","year":"2020","journal-title":"IEEE J. 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