title: Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters.
abstract: 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.
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
authors.forename: Nadi
authors.initials: N
authors.lastname: Sadr
mesh_terms: D000328:Adult; D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D012119:Respiration; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology