Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals.
A method for the detection of sleep apnoea, suitable for use in the home environment, is presented. The method automatically analyses night-time electrocardiogram (ECG) and oximetry recordings and identifies periods of normal and sleep-disordered breathing (SDB). The SDB is classified into one of six classes: obstructive, mixed and central apnoeas, and obstructive, mixed and central hypopnoeas. It also provides an estimated apnoea, hypopnoea and apnoea-hypopnoea index. The basis of the method is a pattern recognition system that identifies episodes of apnoea by analysing the heart variability, an ECG-derived respiration signal and blood oximetry values. The method has been tested on 183 subjects with a range of apnoea severities who have undergone a full overnight polysomnogram study. The results show that the method separates control subjects from subjects with clinically significant sleep apnoea with a specificity of 83 per cent and sensitivity of 95 per cent. These results demonstrate that home-based screening for sleep apnoea is a viable alternative to hospital-based tests with the added benefit of low cost and minimal waiting times.
de Chazal
Philip
P
BiancaMed, NovaUCD, University College Dublin, Belfield, Dublin 4, Ireland. philip.dechazal@biancamed.com
Heneghan
Conor
C
McNicholas
Walter T
WT
D000328:Adult; D004562:Electrocardiography / Q000379:methods*; D001330:Electronic Data Processing; D005260:Female; D006699:Home Care Services; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods; D010092:Oximetry; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology