An automatic multi-lead electrocardiogram segmentation algorithm based on abrupt change detection

Automatic detection of electrocardiogram (ECG) waves provides important information for cardiac disease diagnosis. In this paper a new algorithm is proposed for automatic ECG segmentation based on multi-lead ECG processing. Two auxiliary signals are computed from the first and second derivatives of several ECG leads signals. One auxiliary signal is used for R peak detection and the other for ECG waves delimitation. A statistical hypothesis testing is finally applied to one of the auxiliary signals in order to detect abrupt mean changes. Preliminary experimental results show that the detected mean changes instants coincide with the boundaries of the ECG waves.