Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features

Detection of obstructive sleep apnea can be performed through heart rate variability analysis, since fluctuations of oxygen saturation in blood cause variations in the heart rate. Such variations in heart rate can be assessed by means of time-frequency analysis implemented with time-frequency distributions belonging to Cohen's class. In this work, dynamic features are extracted from time frequency distributions in order to detect obstructive sleep apnea from ECG signals recorded during sleep. Furthermore, it is applied a methodology to measure the relevance of each dynamic feature, before the implementation of k-nn classifier used to recognize the normal and pathologic signals. As a result, the proposed method can be applied as a simple diagnostic tool for OSA with a high accuracy (up to 92.67%) in one-minute intervals.