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 a-level neyman-pears on 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 msec, 3.8 msec and 6.1 msec for p-wave, qrs complex and t-wave, respectively. also the method was applied to day general hospital high resolution holter data (more than 1,500,000 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 (1^6,000 samples/sec) are important merits of the proposed algorithm.