Sleep apnea detection using with EEG, ECG and respiratory signals

Sleep apnea is one of the major sleep disorders of today. The diagnosis of sleep apnea is performed by specialist physicians. This situation extends the duration of the diagnosis. To shorten this period and at the same time to avoid the mistakes that may occur in diagnosis, an automated decision support system has been considered in the diagnosis and classification of sleep apnea. In this study, ECG signal was analyzed to obtain Heart Rate Variability (HRV) signal and the power spectral density (PSD) of this signal was examined. It has been observed that the low and high frequency energy ratios are different in the PSD of examined HRV. Parallel to this analysis, the energy of the respiratory signal is obtained and it is understood that there is a significant energy exchange in the apnea cases. However, the powers of the frequency bands in the EEG signal were found separately and the ratios of these bands to each other were calculated. In the analysis, it was observed that the ratios of alpha and beta bands to each other were different between apnea and non-apnea periods. By using these differences, an artificial neural network (ANN) algorithm is constructed to diagnose and classify the sleep apnea. This algorithm was tested on two patient data; ANN was trained and tested separately for each patient. As a result, it was determined that the average accuracy rate of ANN is high.