Automatic analysis of cardiac repolarization morphology using Gaussian mesa function modeling.

A novel fully automated method for wave identification and extraction from electrocardiogram (ECG) waveforms is presented. This approach implements the combined use of a new machine-learning algorithm and of specified parameterized functions called Gaussian mesa functions (GMFs). Individual cardiac cycle waveforms are broken up into GMFs using a generalized orthogonal forward regression algorithm; each individual GMF is subsequently identified (wave labeling) and analyzed for feature and morphologic extraction. The GMF associated with the repolarization waveform of the main vector lead, based on principal components analysis, was analyzed, and a set of morphologic parameters were derived under 2 experimental settings: first, in 100 digital 12-lead ECG Holter recordings acquired during three 24-hour periods (baseline and after 160 and 320 mg of sotalol) from 38 healthy subjects; second, in drug-free 12-lead resting ECGs from 100 genotyped long QT syndrome (LQTS) patients (50 each with LQT1 and LQT2). QT-interval duration was measured using an on-screen method applied to the global representative beats and reviewed by a senior cardiologist. QTci (individual correction) was used for analysis. All parameters in the sotalol test showed highly significant differences between the time of peak plasma concentration (Tmax) and baseline ECGs; however, the dynamic pattern of individual parameters followed different patterns. The LQTS test confirmed the results of the sotalol test, showing that GMF-based repolarization parameters were strongly modified as compared with healthy controls. In particular, T-wave width and descending phase of repolarization were more prolonged in LQT2 compared to LQT1.