Paperdoi:10.1186/s41606-026-00179-6
ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea
Sleep Science and Practice (2026-06-11)
TL;DR
PSG recordings from 53 participants (OSA-only, OSA+MDD, controls) cut into 5-min pure-stage segments. Deep sleep was the most discriminative stage. PPG vascular-stiffness features in deep sleep separated controls from OSA with 100% accuracy; for MDD within OSA, PPG peripheral-pulse timing delays + one ECG entropy feature gave 89.33% accuracy, AUC 0.91.
- Form factor: finger (PSG)
Abstract
Background
Electrocardiogram (ECG) and photoplethysmogram (PPG) signals offer moderate ability to distinguish Obstructive Sleep Apnea (OSA) patients, including those with comorbid Major Depressive Disorder (MDD), from healthy individuals. This study investigated whether analyzing these signals during specific sleep stages could improve the detection of MDD within an OSA cohort.
Methods
Polysomnography data from 53 participants (OSA-only, OSA with MDD, and healthy controls) was analyzed. Recordings were segmented into 5-min intervals of pure sleep stages.
Results
Deep-Sleep was identified as the most discriminative stage, yielding the highest number of significant features and with strong effect sizes for both ECG and PPG. Using Deep-Sleep PPG features related to vascular stiffness, distinguishing Controls from OSA was performed with 100% accuracy. For the more complex task of identifying MDD within the OSA group, combining PPG timing delays in the peripheral pulse wave and one ECG entropy feature achieved 89.33% accuracy, with an AUC of 0.91.
Conclusion
This single-site research demonstrates that deep-sleep-stage-specific analysis improves the power of ECG and PPG signals to differentiate between Controls, OSA, and OSA with MDD. This framework has the potential for developing an accurate ECG and PPG-based system to screen for depression in patients with sleep disorders, overcoming delays in the assessment due to long waiting periods in sleep clinics. The translation of this research requires a large, multi-site database for the evaluation of wearable devices for this application.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0
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Cite this paper
Shaw, V., Ngo, Q. C., Pah, N. D., Khandoker, A. H., Mahapatra, P. K., Pankaj, D., & Kumar, D. K. (2026). ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea. Sleep Science and Practice. https://doi.org/10.1186/s41606-026-00179-6
Shaw, Vikash, Quoc Cuong Ngo, Nemuel Daniel Pah, Ahsan Habib Khandoker, Prasant Kumar Mahapatra, Dinesh Pankaj, and Dinesh K. Kumar. 2026. "ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea." Sleep Science and Practice. https://doi.org/10.1186/s41606-026-00179-6.
Shaw, V., Ngo, Q. C., Pah, N. D., Khandoker, A. H., Mahapatra, P. K., Pankaj, D. and Kumar, D. K. (2026) 'ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea', Sleep Science and Practice. doi: 10.1186/s41606-026-00179-6.
Shaw, Vikash, et al. "ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea." Sleep Science and Practice, 2026. https://doi.org/10.1186/s41606-026-00179-6.
V. Shaw et al., "ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea," Sleep Science and Practice, 2026, doi: 10.1186/s41606-026-00179-6.
@article{shaw2026during,
title = {{ECG and PPG during deep sleep for computerised detection of comorbid major depressive disorder in obstructive sleep apnea}},
author = {Shaw, Vikash and Ngo, Quoc Cuong and Pah, Nemuel Daniel and Khandoker, Ahsan Habib and Mahapatra, Prasant Kumar and Pankaj, Dinesh and Kumar, Dinesh K.},
year = {2026},
journal = {Sleep Science and Practice},
doi = {10.1186/s41606-026-00179-6}
}Added to the ChatPPG research hub
