ApSense: Data-Driven Algorithm in PPG-Based Sleep Apnea Sensing

Abstract

Detecting obstructive sleep apnea (OSA) is essential for diagnosing and managing sleep health. Traditionally, this involves clinical settings with hardly accessible processes. We propose that automated detection of OSA events is achievable using features extracted from fingertip photoplethysmography (PPG) signals combined with modern deep learning (DL) techniques. Utilizing two benchmark datasets with extensive PPG recordings, we introduce ApSense, a DL model designed for OSA event onset recognition from PPG features. ApSense presents a custom neural architecture and domain-specific feature extraction from PPG waveforms. We benchmark it against state-of-the-art (SOTA) algorithms, including RRWaveNet, PPGNetSA, AIOSA, DRIVEN, and LeNet-5. In our evaluations, ApSense demonstrated improved sensitivity, specificity, and area under the receiver operating characteristic (AUROC) on the test datasets. Furthermore, an ablation study highlighted strategic customizations of ApSense, enhancing its performance and adaptability to different datasets. ApSense demonstrates high reliability, as its outstanding results were confirmed even in high-variance datasets. By detecting OSA events, ApSense enables the estimation of the predicted Apnea-Hypopnea Index (pAHI), which can be used for pre-screening individuals for sleep apnea in a low-cost setup. ApSense shows the potential for PPG-based OSA detection and clinical applications for pre-screening in the future.