A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal

Abstract

In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.

Identification of Patients with Myocardial Infarction

“…The literature displays reports showing different ways to address the identification of the infarcted area. So, Safdarian et al [10] applied feature extraction from ECG signals and several classifiers for the location of the MI; these authors reached over 76% for accuracy, in test data for localization in four classes (Healthy, Anterior, Inferior, and Posterior). Similarly, Arif et al [9] presented an automatic method for MI localization using K-nearest neighbor, obtaining an accuracy of 98.3% for localization.…”

“…Furthermore, Muhammad et al [9] presented automatic detection and localization of MI using k-Nearest Neighbor (KNN) classifier and Time Domain features of each beat in the ECG signal, such as T-wave amplitude. In a recent work, Safdarian et al [10] proposed two new features, i.e., T-wave integral and total integral as extracted features from one ECG cycle for the infarcted area detection and localization.…”

Methodology of Filtering the Noise and Utilization of STEMI in the Myocardial Infarction

“…Preprocessing and feature extraction is also done. [10,6,3,7] Sign for ischemia and infarction are numerous. [11].…”

Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records

“…In our work, we are committed to developing a practical, medical-grade detection algorithm of myocardial infarction. With the increasing popularity of machine learning, many researchers [3][4][5][6][7][8][9][10][11][12][13][14][15] have applied machine learning techniques to the detection of myocardial infarction and achieved promising results.…”

“…The myocardial infarction was classified by Gaussian mixture models (GMMs) and their accuracy, sensitivity and specificity achieved 82.50%, 85.71%, and 79.82%, respectively. Safdarian N et al [6] used two new features, T-wave integral and total integral, in the detection of myocardial infarction; accuracy on the test set reached 94.74%. In a study by Sharma L Net al [3], a novel feature extraction method, based on multi-scale wavelet energy and multi-scale covariance matrix, was proposed.…”