Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

Abstract

In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

“…Their method showed 94.4% classification accuracy with J48 decision tree model for the diagnosis of MI. The approach presented in [59] utilized the evaluation of multiscale energy and eigenspace (MEES) features. The suggested method used support vector machine (SVM) classifier with RBF kernel and achieved 96.15% classification accuracy.…”

“…Moreover, the methods suggested in [5,[56][57][58][59] used ECG recordings of the multiple leads. However, our method uses only lead-2 ECG recordings, which makes our method less complex than multiple leads methods.…”

Cardiac arrhythmia classification from multilead ECG using multiscale non-linear analysis

“…By analyzing all leads simultaneously, we can detect the presence of heart diseases. Various methods have been proposed for automated detection of heart disorders using multilead ECG, such as tensor rank analysis [7], ST segment analysis [8], principal component multivariate sample entropy (PMMSE) [9], neural network approaches [10] and wavelet based analysis [11], [12] etc. The methods reported in [8], and [10] requires evaluation of various morphological features such as T-wave amplitude, ST-slope, Q-wave amplitude etc.…”

Detection of cardiac ailments from multilead ECG using diagnostic eigen error features

“…Myocardial infarction (MI) and hypertrophy (HT) are life threatening cardiac ailments. MI is due to the obstruction in coronary artery of heart [3] and the symptoms of MI are T-wave inversion, ST-segment elevation and abnormal Q-waves [4]. Due to hypertrophy, the P-wave and QRS-complex amplitudes are higher than that of normal sinus rhythm [1].…”

“…The ST-segment analysis based MI detection has been proposed in [13]. The multiscale energy and eigenspace approach based MI detection was proposed in [4]. A tensor based feature extraction and classification of cardiac ailments has been proposed [14].…”