A Fast Principal Component Analysis Method For Calculating The ECG Derived Respiration.

In this paper, we present a principal component analysis (PCA) method for estimating the respiration from overnight ECG recording. In comparison to other published methods, our method is very fast to compute and has low memory requirements, which makes it suitable for processing long duration ECG recordings. We used our method to derive respiratory features for the ECG which were then used to identify epochs of sleep apnoea from the ECG. Three classifiers including the extreme learning machine (ELM), linear discriminant analysis, and support vector machine were used to detect sleep apnoea. The method was evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. Our PCA method obtained the highest accuracy of 74% by ELM classifier. We conclude that the fast PCA method is useful to apply PCA to long ECG recordings.