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Cerqueira, M.D., et al. (2002) Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart. Circulation, 105, 539-542.
http://dx.doi.org/10.1161/hc0402.102975

has been cited by the following article:

  • TITLE: 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

    AUTHORS: Naser Safdarian, Nader Jafarnia Dabanloo, Gholamreza Attarodi

    KEYWORDS: ECG Signal Classification, Signal Processing, Myocardial Infarction, Features Extraction, Neural Network

    JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.7 No.10, August 6, 2014

    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.