Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram

• Published in: Physiological measurement Vol. 25; no. 4; pp. 967 - 983
• Main Authors: Chazal, Philip de, Penzel, Thomas, Heneghan, Conor
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2004
• Subjects: Adult; Aged; Algorithms; Automation; Electrocardiography - methods; Female; Humans; Male; Middle Aged; Observer Variation; Respiration; Sensitivity and Specificity; Sleep Apnea, Obstructive - classification; Sleep Apnea, Obstructive - diagnosis
• ISSN: 0967-3334; 1361-6579
• DOI: 10.1088/0967-3334/25/4/015

An automated classification algorithm is presented which processes short-duration epochs of surface electrocardiogram data derived from polysomnography studies, and determines whether an epoch is from a period of sleep disordered respiration (SDR) or normal respiration (NR). The epoch lengths considered were 15, 30, 45, 60, 75, and 90 s. Epochs were labeled as 'NR' or 'SDR' by a human expert, based on standard polysomnography interpretation rules. The automated classification algorithm was trained and tested on a database of 70 overnight ECG recordings from subjects with and without obstructive sleep apnoea syndrome (35 used for training, 35 for independent validation). Depending on the epoch length, the classifier correctly labeled between 87% (15 s epochs) and 91% (60 s epochs) of the epochs in the test set. Accuracy was lowest for the shortest (15 s) and longest (90 s) epoch lengths, but the analysis was relatively insensitive to choice of epoch length. The classifications from these epochs were combined to form an overall summary measure of minutes-of-SDR, allowing per-subject classification.