Abstract
Computer-aided diagnosis have emerged as additional help to the medical domain. Over the years ECG signal being simple, cheap, and noninvasive, is explored for the diagnosis of heart diseases. Multilead simultaneously acquired ECG improves the accuracy in diagnosis of heart diseases. The paper focuses on diagnosing Myocardial Infarction from multilead ECG using Multilayer Perceptron Model. In the present work, the proposed feature vector used for the classification includes QRS point score as one of the feature along with the other morphological features. The study is an attempt to discuss the utility of point score as a feature in the feature vector for classification of Myocardial Infarction disease from ECG signal to enhance the performance of classification. The results show significant improvement when the point score is used in the feature vector. The model is evaluated with 34 ECG signals of normal subjects and 33 ECG signals of MI patients from PTB database, collected from physionet. The classification accuracy is above 95% including point score feature and the same is less than 85% excluding the point score in all the leads. The inclusion of point score as a feature for diagnosing Myocardial infarction results in better accuracy.
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Kasar, S.L., Joshi, M.S., Mishra, A., Mahajan, S.B., Sanjeevikumar, P. (2018). Performance Enhancement for Detection of Myocardial Infarction from Multilead ECG. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_66
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DOI: https://doi.org/10.1007/978-981-10-7868-2_66
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