On the Detection of Myocadial Scar Based on ECG/VCG Analysis

Abstract

Abstract-In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10-fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%).

Bagged tree classi cation of arrhythmia using wavelets for denoising, compression, and feature extraction

“…The usage of wavelet-compressed data from the St. Petersburg Institute of Cardiological Technics 12lead arrhythmia database was our first step. In general, the MIT-BIH arrhythmia database is used by most research that is performed on ECG signals [1][2][3][4][5][6][7][8][9][10][11][12]14,16,19,[21][22][23]25,26], and classification is executed on a single channel [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][21][22][23][24][25][26][27]. Our research was performed on 12 channels.…”

“…Generally, neural networks are preferred for classification [1][2][3]6,11,12,19,25,27]. The SVM [4,5,8,14,16,18,21,22,24] is another popular technique for classification of arrhythmia. Random forest [9], linear classifier [10], morphology consistency evaluation [13], cluster and centroid identification [15], linear discriminant analysis [17], threshold based classifier [20], KNN [7,23], and naive Bayes [26] are the other classification methods that are used in related works.…”

“…Dima et al [18] developed a system for detection of the presence of a myocardial scar from ECGs and vector cardiograms. Four distinct methods were implemented.…”

Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention

“…However, it is time consuming and error-prone to analyze ECGs in practice, therefore, computer-aided algorithms may offer a promising way to improve the efficiency and accuracy of ECG analyzing. The algorithms for ECG analyzing typically contain three steps, which are preprocessing, feature extraction and classification [1][2][3][4][5]. Among these, the feature extraction is a critical step, for which many methods have been proposed, such as morphology information [2], temporal and frequency features [3], high order statistical features [4] and wavelet features [5].…”

Methods for derivation of orthogonal leads from 12-lead electrocardiogram: A review

“…Designed Support Vector Machine (SVM) model achieves sensitivity 82.36% and specificity 77.36%. Another robust classification algorithm presented authors in [17]. For detection MI scar, they used complex model based on SVM and using ECG and derived VCG features.…”