Detection of sleep apnea episodes from multi-lead ECGs considering different physiological influences.
OBJECTIVE
This article deals with recognition of sleep apnea, using solely information available from multi-lead ECGs.


METHODS
Characteristic variations in heart rhythm and amplitude of the ECG are compared with respect to their diagnostic accuracy by means of an ROC analysis that is performed on a local similarity index. In 38 8-lead ECGs, each minute is classified with respect to occurrence of apnea events and the result is validated against expert annotations derived from synchronized polysomnographic recordings. Moreover, the results are compared to those obtained from the well known Physionet apnea-ECG database.


RESULTS
Whereas the effect of amplitude modulation yields consistent results on both data sets (ROC-area 89.0% vs. 88.3%), a remarkable loss in performance is observed for the frequently applied heart rhythm (89.8% vs. 77.9%). Examples illustrating the reasons for this difference are given and discussed. With respect to aggregation of multi-lead information, two methods (PCA vs. averaging) are compared. The results indicate that averaging performs better (89.3%) than the adaptively estimated PCA (87.2) even when applied to a reduced set of leads.


CONCLUSIONS
It is concluded that sleep apnea recognition from heart rhythm should always be complemented by analysis of the amplitude variations of the ECG.
Dickhaus
H
H
Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany.
Maier
C
C
D001341:Autonomic Nervous System / Q000502:physiology; D019992:Databases as Topic; D004562:Electrocardiography / Q000295:instrumentation*; D015716:Electrocardiography, Ambulatory; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D012372:ROC Curve; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology; D013995:Time