  This paper contributes to developing fingertip PPG-based obstructive sleep
apnea (OSA) event onset recognition. We introduce a novel algorithm ApSense,
embodying medical domain knowledge-based feature extraction from PPG waveform
and tailor-made deep neural architecture for OSA onset recognition. ApSense
outperforms current state-of-the-art techniques, including MiniRRWaveNet,
PPGNetSA, and MiniAIOSA, achieving better sensitivity, specificity, and area
under the receiver operating characteristic (AUROC) in two benchmark datasets.
Our ablation study further presents a strategic customization of ApSense,
allowing a more fine-grained framework for each benchmark dataset. Through the
experimental pilots, we present practical guidance in applying and optimizing
ApSense for other PPG-based applications, not limited to OSA onset recognition.
