Real-world sleep phenotyping in autism: compliance, domain shift, and scalable wearable biomarkers

Sanketh Vedula, Andrew Kim, Manoj Kumar, Olga Troyanskaya, Guillermo Sapiro
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:2055-2108, 2026.

Abstract

Sleep is a scalable target for digital health, but real-world sleep phenotyping remains challenging, especially in autism spectrum disorder (ASD), where laboratory polysomnography (PSG) is difficult to deploy longitudinally. We study ambulatory sleep sensing in the Simons Sleep Project, a multimodal dataset of 200 children recorded over 14 nights using dry-electrode electroencephalography (EEG) headbands and wrist-worn sensors (photoplethysmography (PPG), inertial measurement units (IMU), temperature). We first show that EEG headband compliance declines systematically overnight and is associated with behavioral measures, introducing structured missingness and biasing sleep estimates. In contrast, wrist-worn sensors maintain near-complete adherence. We then demonstrate a substantial domain shift: sleep-staging models trained on clinical-grade data show limited off-the-shelf transfer to wearable recordings. However, this shift is learnable with limited supervision. For EEG, zero-shot models perform near chance, while fine-tuning on 30–50 participants (270-450 nights) recovers performance; fine-tuning the public SleepFM foundation model on 102 participants ($\sim$9 nights per participant) achieves a macro F1 of 0.780. For PPG, the public PaPaGei foundation model achieves only 0.375 macro F1 out-of-the-box, whereas supervised models trained on PPG features reach 0.710, further indicating limited off-the-shelf transfer from clinical-grade pretraining to wearable recordings. Watch sensor ablations identify PPG as the most informative standalone peripheral signal, with PPG+IMU capturing most staging information from the wrist. EEG channel analysis further shows that frontal–occipital derivations outperform prefrontal channels, informing minimal-channel designs. Together, these results establish a modality hierarchy for ambulatory sleep phenotyping: wrist-worn PPG+IMU provides a scalable backbone for longitudinal monitoring, while EEG remains valuable for micro-architectural analysis when tolerated. We anticipate these lessons on compliance-aware design, domain adaptation, and principled sensor selection to generalize to other vulnerable populations and at-home deployment of digital health studies.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-vedula26a, title = {Real-world sleep phenotyping in autism: compliance, domain shift, and scalable wearable biomarkers}, author = {Vedula, Sanketh and Kim, Andrew and Kumar, Manoj and Troyanskaya, Olga and Sapiro, Guillermo}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {2055--2108}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/vedula26a/vedula26a.pdf}, url = {https://proceedings.mlr.press/v340/vedula26a.html}, abstract = {Sleep is a scalable target for digital health, but real-world sleep phenotyping remains challenging, especially in autism spectrum disorder (ASD), where laboratory polysomnography (PSG) is difficult to deploy longitudinally. We study ambulatory sleep sensing in the Simons Sleep Project, a multimodal dataset of 200 children recorded over 14 nights using dry-electrode electroencephalography (EEG) headbands and wrist-worn sensors (photoplethysmography (PPG), inertial measurement units (IMU), temperature). We first show that EEG headband compliance declines systematically overnight and is associated with behavioral measures, introducing structured missingness and biasing sleep estimates. In contrast, wrist-worn sensors maintain near-complete adherence. We then demonstrate a substantial domain shift: sleep-staging models trained on clinical-grade data show limited off-the-shelf transfer to wearable recordings. However, this shift is learnable with limited supervision. For EEG, zero-shot models perform near chance, while fine-tuning on 30–50 participants (270-450 nights) recovers performance; fine-tuning the public SleepFM foundation model on 102 participants ($\sim$9 nights per participant) achieves a macro F1 of 0.780. For PPG, the public PaPaGei foundation model achieves only 0.375 macro F1 out-of-the-box, whereas supervised models trained on PPG features reach 0.710, further indicating limited off-the-shelf transfer from clinical-grade pretraining to wearable recordings. Watch sensor ablations identify PPG as the most informative standalone peripheral signal, with PPG+IMU capturing most staging information from the wrist. EEG channel analysis further shows that frontal–occipital derivations outperform prefrontal channels, informing minimal-channel designs. Together, these results establish a modality hierarchy for ambulatory sleep phenotyping: wrist-worn PPG+IMU provides a scalable backbone for longitudinal monitoring, while EEG remains valuable for micro-architectural analysis when tolerated. We anticipate these lessons on compliance-aware design, domain adaptation, and principled sensor selection to generalize to other vulnerable populations and at-home deployment of digital health studies.} }
Endnote
%0 Conference Paper %T Real-world sleep phenotyping in autism: compliance, domain shift, and scalable wearable biomarkers %A Sanketh Vedula %A Andrew Kim %A Manoj Kumar %A Olga Troyanskaya %A Guillermo Sapiro %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-vedula26a %I PMLR %P 2055--2108 %U https://proceedings.mlr.press/v340/vedula26a.html %V 340 %X Sleep is a scalable target for digital health, but real-world sleep phenotyping remains challenging, especially in autism spectrum disorder (ASD), where laboratory polysomnography (PSG) is difficult to deploy longitudinally. We study ambulatory sleep sensing in the Simons Sleep Project, a multimodal dataset of 200 children recorded over 14 nights using dry-electrode electroencephalography (EEG) headbands and wrist-worn sensors (photoplethysmography (PPG), inertial measurement units (IMU), temperature). We first show that EEG headband compliance declines systematically overnight and is associated with behavioral measures, introducing structured missingness and biasing sleep estimates. In contrast, wrist-worn sensors maintain near-complete adherence. We then demonstrate a substantial domain shift: sleep-staging models trained on clinical-grade data show limited off-the-shelf transfer to wearable recordings. However, this shift is learnable with limited supervision. For EEG, zero-shot models perform near chance, while fine-tuning on 30–50 participants (270-450 nights) recovers performance; fine-tuning the public SleepFM foundation model on 102 participants ($\sim$9 nights per participant) achieves a macro F1 of 0.780. For PPG, the public PaPaGei foundation model achieves only 0.375 macro F1 out-of-the-box, whereas supervised models trained on PPG features reach 0.710, further indicating limited off-the-shelf transfer from clinical-grade pretraining to wearable recordings. Watch sensor ablations identify PPG as the most informative standalone peripheral signal, with PPG+IMU capturing most staging information from the wrist. EEG channel analysis further shows that frontal–occipital derivations outperform prefrontal channels, informing minimal-channel designs. Together, these results establish a modality hierarchy for ambulatory sleep phenotyping: wrist-worn PPG+IMU provides a scalable backbone for longitudinal monitoring, while EEG remains valuable for micro-architectural analysis when tolerated. We anticipate these lessons on compliance-aware design, domain adaptation, and principled sensor selection to generalize to other vulnerable populations and at-home deployment of digital health studies.
APA
Vedula, S., Kim, A., Kumar, M., Troyanskaya, O. & Sapiro, G.. (2026). Real-world sleep phenotyping in autism: compliance, domain shift, and scalable wearable biomarkers. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:2055-2108 Available from https://proceedings.mlr.press/v340/vedula26a.html.

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