Sleep Apnea Detection System
End-to-end signal-processing pipeline for automated sleep apnea detection from audio using Mel spectrograms and a custom CNN.
Stack
PyTorch, CNN, Mel Spectrograms, Apple MPS
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The Process
Developed an end-to-end signal-processing pipeline for automated sleep apnea detection from audio. The system handles segmentation, Mel spectrogram extraction, and CNN inference. It automates 30-second epoch segmentation and event merging to compute a clinical Apnea-Hypopnea Index.
The Outcome
Successfully deployed on Apple Silicon MPS for real-time inference. For example, it correctly classified a recording with 42 events over 7.19 hours resulting in an AHI of 5.83 (Mild). Tuned via F1/ROC-AUC.