title: Sleep apnoea episodes recognition by a committee of ELM classifiers from ECG signal.
abstract: This paper describes a system for the recognition of sleep apnoea episodes from ECG signals using a committee of extreme learning machine (ELM) classifiers. RR-interval parameters (heart rate variability) have been used as the identifying features as they are directly affected by sleep apnoea. The MIT PhysioNet Apnea-ECG database was used. A committee of five ELM classifiers has been employed to classify one-minute epochs of ECG into normal or apnoeic epochs. Our results show that the classification performance from the committee of networks was superior to the results of a single ELM classifier for fan-outs from 1 to 100. Classification performance reached a plateau at a fan-out of 10. The maximum accuracy was 82.5% with a sensitivity of 81.9% and a specificity of 82.8%. The results were comparable to other published research with the same input data.
authors.forename: Nadi
authors.initials: N
authors.lastname: Sadr
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
authors.forename: Andre
authors.initials: A
authors.lastname: van Schaik
authors.forename: Paul
authors.initials: P
authors.lastname: Breen
mesh_terms: D000328:Adult; D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology*