Data–Driven Multimodal Sleep Apnea Events Detection: Synchrosquezing Transform Processing and Riemannian Geometry Classification Approaches

Abstract

A novel multimodal and bio–inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data–driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real–time unimodal brainwaves (EEG only) decoding in brain–computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data–driven and bio–inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine–learning–related classification.