# Combining Signals for EEG-Free Arousal Detection during Home Sleep Testing: A Retrospective Study

## TL;DR

This study shows that combining simple physiological signals can help detect sleep arousals without EEG during home sleep tests.

## Contribution

The study introduces a method to detect arousal using non-EEG signals in home sleep testing with deep learning.

## Key findings

- Combining multiple signals improved arousal detection performance over single-signal models.
- Thoracic effort, heart rate, and a wake/sleep indicator achieved 61.59% precision and 56.46% recall.
- The method offers a competitive alternative to EEG-based arousal detection for home sleep devices.

## Abstract

Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. Objectives: The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. Methods: We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. Results: The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. Conclusions: This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection.