Brain–Heart Aging During Sleep Predicts Incident Stroke

Matteo Saibene, Gouthamaan Manimaran, Sadasivan Puthusserypady, Ying Gu, Maria Helena Dominguez Vall-Lamora, Martin Ballegaard, Jakob E. Bardram

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Keywords: Stroke, Biological Age, Deep Learning

TL;DR: An attention-based model integrating EEG–ECG interactions during sleep reveals that an elevated brain–heart biological age (HR 1.25) significantly predicts incident stroke.

Abstract:

Detecting stroke risk remains a major challenge in preventive medicine.
In this work, we introduce a novel computational approach for modeling the effect of aging to identify patients at risk of stroke by analyzing the intricate relationship between brain and heart dynamics during sleep. We analyzed whole-night Polysomnography (PSG) data focusing on sleep stage transitions, to capture changes in cortical and autonomic functions. Using an attention-based model tuned for age estimation, we identify patients at risk of stroke.
The model has been tested on 782 patients and a systematic ablation study was performed to evaluate predictive performance across different signal modality configurations and sleep stages.
Results from this study indicate that the patients at risk of stroke show pronounced aging effects,
suggesting that Brain-Heart Interaction (BHI) during sleep may be applied on a population level as a novel biomarker to identify patients at risk of stroke.

Track: 7. General Track

Registration Id: 8XNZ7PH3B7P

Submission Number: 25