Abstract
Myocardial infarction (MI) is the alarming symbol of heart attack which causes the heart muscles to get damaged due to MI which leading to death. The early diagnosis and detection of symptoms based on myocardial infarction are extremely necessary to reduce probability of death of the patient. The main objective of this work is to develop a classification framework for electrocardiogram (ECG) signals using morphological, time domain, and empirical mode decomposition (EMD) features to classify between MI and healthy control (HC). The PTBDB database was used to test the whole experiment. A logistic model trees (LMT) classifier is proposed to classify the MI and HC samples. As per the experimental study for detection of MI and HC, the classifier achieves an accuracy of 98.75% over fusion of features.
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Sahoo, S., Patra, G.R., Mohanty, M., Samanta, S. (2022). Automated Detection of Myocardial Infarction with Multi-lead ECG Signals using Mixture of Features. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_35
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DOI: https://doi.org/10.1007/978-981-19-0825-5_35
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