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

Abstract

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 α-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.

Euclidean Distance Measure for the Electrocardiogram Fiducial Points Detection in the Phase-Space Formed with the Derivative Rule

“…LE is smaller than one sample (4 ms) for the Q points, approximately one sample for the R points and approximately three samples for the S points. The results for the Se, PPV and LE values for the QRS-complex delineation are comparable with the results of the known previous investigations [6,9,13,14,16,17,19].…”

Current Methods of Ambulatory Electrocardiogram Monitoring. Technical Aspects

“…Низкая (250-500 Гц) частота оцифровки сигнала вызывает трудно-сти при анализе стимулов электрокардиостимулятора (ЭКС). Для улучшения качества ЭКГ-сигнала и по-давления шума производители стремятся увеличить частоту дискретизации до 500 Гц и более, разрабаты-вают новые алгоритмы дискретизации сигнала и де-текции [9]. В зависимости от типа ХМ-ре гистратора и ЭКГ-кабеля число регистрируемых реальных и/или модифицированных ЭКГ-отведений варьирует от 1 до 12 (наиболее часто используются 2-3).…”