Detection of Obstructive Sleep Apnea Based on ECG System Dynamics, Hybrid Signal Processing and Neural Networks

This study aims to develop a new approach for detecting OSA automatically with ECG system dynamics disparity. First, tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space of three-dimension (3D) are combined for the feature extraction, which can carry clinically relevant information about the anomalies present in the OSA ECG recordings. Second, dynamic modeling and identification of ECG systems are carried out using neural networks. By constructing on a bank of dynamical estimators, normal and OSA ECG signals will be classified based on their differences in dynamics. Finally, a physionet apnea-ECG database consisting of 70 overnight recordings from 70 participants is used to evaluate the outcomes. By using a cross-validation scheme of 10 folds, experimental results indicate that neural network based classifier along with proposed features achieves higher accuracy, sensitivity, and specificity values of 98.27%, 97.68%, and 98.63%, respectively. Results validate that the proposed method may serve as an alternative to PSG to detect OSA automatically in clinical settings.