Non-invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands.

In this paper, we used ECG signals and repiratory inductance plethysmography (RIP) or respiratory bands. We evaluated the performance of the signals individually as well as different combinations of features and signals for sleep apnoea detection. We implemented two methods (QRS area, and fast principal component analysis (PCA) methods) for estimating the ECG derived respiratory (EDR) signal and the cardiopulmonary coupling (CPC) spectrum. We then extracted features from the time and frequency representations of the ECG and RIP signals. Finally, we applied different features sets to a linear discriminant analysis (LDA) for classification. The results were examined on the MIT PhysioNet Apnea-ECG database. Apnoea classification was carried out using leave-one-record-out crossvalidation approach. The highest performance of our algorithm was achieved using the RIP and RR-interval features as well as using the RIP and PCA CPC features with an accuracy of 90% and AUC of 0.97. The highest performance results of using only RIP or ECG features achieved an accuracy of 87% and AUC of 0.95. We conclude that although ECG sensors are more convenient for patients in sleep studies, using both RIP and ECG sensors enhances the performance results for automated diagnosis of sleep apnoea.