Optimal Delineation of Ambulatory Holter ECG Events via False-Alarm Bounded Segmentation of a Wavelet-Based Principal Components Analyzed Decision Statistic

Abstract

The aim of this study is to develop and describe a new ambulatory holter electrocardiogram (ECG) events detection-delineation algorithm with the major focus on the bounded false-alarm probability (FAP) segmentation of an information-optimized decision statistic. After implementation of appropriate preprocessing methods to the discrete wavelet transform (DWT) of the original ECG data, a uniform length sliding window is applied to the obtained signal and in each slid, six feature vectors namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated to construct a newly proposed principal components analyzed geometric index (PCAGI) by application of a linear orthonormal projection. In the next step, the α-level Neyman-Pearson classifier (which is a FAP controlled tester) is implemented to detect and delineate QRS complexes. The presented method was applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.96% and P+ = 99.96% are obtained for the detection of QRS complexes, with the average maximum delineation error of 5.7, 3.8 and 6.1 m for P-wave, QRS complex and T-wave, respectively. Also, the proposed 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% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process in a widespread values of signal to noise ratio (SNR), reliable robustness against strong noise, artifacts and probable severe arrhythmia(s) of high resolution holter data and the processing speed 155,000 samples/s can be mentioned as important merits and capabilities of the proposed algorithm.

Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

“…Other innovative methods by Mehta et al [7], Chouhan et al [8] and Mehta-Lingayat [9][10][11] elaborated some efficient algorithms for the aim of ECG signal P, QRS and T waves detection as well as their robust delineation (segmentation) based on Support Vector Machine as the discrimination method and signal information-based measure as the feature of the detection-delineation process. As some other proposed methods, the algorithms based on mathematical models [12], Hilbert transform and the first derivative [13][14][15][16], multiple higher order moments [17], second order derivative [18], wavelet transform and the filter banks [19][20][21][22], combination of signal derivatives and multi-resolution digital filters with non-parametric detection algorithms [23][24][25][26], soft computing (Neuro-fuzzy, genetic algorithm) [27], Hidden Markov Models (HMM) application [28] can be mentioned as the recent studies. In heart sound analysis, a major problem is the robust detection of the incidence (detection) and onset-offset (delineation or segmentation) locations of the genuine sounds of each cardiac cycle existing in the presence of severe heart valvular disorders, heart diseases, high-level measurement noises and circumferential disturbances.…”

Design of a unified framework for analyzing long-duration ambulatory ECG: Application for extracting QRS geometrical features

“…The computerized ECG events detection-delineation algorithm can be implemented for recognizing some certain signal's abnormal symptoms such as T-Wave Alternans (TWA) [2,3], Atrial Fibrillation (AF) [4,5], QT-prolongation [6] and arrhythmia recognition and clustering [7,8]. Up to now, several papers have been proposed numerous procedures for the aim of diagnosing of the ECG events including mathematical models [9], Hilbert transform and the first derivative [10][11][12], statistical higherorder moments [13], second order derivative [14], wavelet transform and the filter banks [15][16][17], soft computing (Neurofuzzy, genetic algorithm) [18], Hidden Markov Models (HMM) application [19], information processing-based approaches such as independent (principal) components analysis and linear (generalized) discriminant analysis [20][21][22] and etc. Some standard databases such as MIT-BIH Arrhythmia Database [23] can be used for Verifying performance of the QRS waveform detection method.…”