| Title | Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters. | ||
| Author | de Chazal, Philip; Sadr, Nadi | ||
| Journal | Annu Int Conf IEEE Eng Med Biol Soc | Publication Year/Month | 2016-Aug |
| PMID | 28268989 | PMCID | -N/A- |
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.