Ambulatory Holter ECG individual events delineation via segmentation of a wavelet-based information-optimized 1-D feature

The aim of this study is to develop and describe a new ambulatory Holter electrocardiogram (ECG) events detection–delineation algorithm via segmentation of an information-optimized decision statistic. After implementation of appropriate pre-processing, a uniform length sliding window is applied to the pre-processed trend and in each slide, some geometrical features of the excerpted segment are calculated to construct a newly proposed Discriminant Analyzed Geometric Index (DAGI), by application of a nonlinear orthonormal projection. Then the αα-level Neyman–Pearson classifier is implemented to detect and delineate QRS complexes. The presented method was applied to several databases and the average values of sensitivity and positive predictivity, Se=99.96% and P+=99.96%, were obtained for the detection of QRS complexes, with an average maximum delineation error of 5.7 ms, 3.8 ms and 6.1 ms for P-wave, QRS complex and T-wave, respectively. Also the method was applied to DAY general hospital high resolution holter data (more than 1500,000 beats, including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC, and Premature Atrial Complex-PAC) and average values of Se=99.98% and P+=99.97% were obtained for QRS detection. High accuracy in a widespread SNR, high robustness and processing speed (146,000 samples/s) are important merits of the proposed algorithm.