Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters.

We investigated using heart rate variability (HRV), ECG derived respiration and cardiopulmonary coupling features (CPC) calculated from night-time single lead ECG signals to classify one-minute epochs for the presence or absence of sleep apnoea. We used the 35 training recordings of the M.I.T. Physionet Apnea-ECG database. Performance was assessed with leave-one-record-out cross-validation. The best classification performance was achieved using the CPC features in conjunction with the time-domain based HRV parameters. The cross-validated results on the 17,045 epochs of the dataset were an accuracy of 89.8%, a specificity of 92.9%, a sensitivity of 84.7%, and a kappa value of 0.78. These results are comparable with best results reported on this database.