Enterprise AI Analysis

A Convolutional Neural Network Framework for Sleep Apnea Detection via Ballistocardiography Signals

This research introduces a novel, weakly supervised framework for detecting sleep apnea using non-invasive Ballistocardiography (BCG) signals. Addressing the critical lack of labeled BCG data, the system leverages synchronized ECG signals as a 'teacher' to generate pseudo-labels for a BCG-specific 1D Convolutional Neural Network. A User-Defined Function (UDF) combining Heart Rate Variability and ECG-Derived Respiration autonomously labels BCG windows. Tested on a public dataset, the CNN achieved 71.8% accuracy against pseudo-labels, estimated at 77.7% against clinical ground truth. This approach validates ECG-based supervision for training low-cost home sensors, circumventing the need for laborious manual medical annotation and paving the way for scalable, continuous sleep disorder monitoring.