Application of multi-feature fusion and random forests to the automated detection of myocardial infarction |
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| Affiliation: | 1. ECG-TECH Corp, Huntington, NY;2. Duke University Medical Center, Durham, NC;3. Aarhus University Hospital, Denmark;4. Physio-control, Redmond, WA;5. Adult Inpatient Medical Services (AIMS) Group, Austin, TX;6. Dept of Clinical Physiology and Nuclear Medicine, Lund University and Skåne University, Hospital, Lund, Sweden;1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore;2. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore;3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;4. Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate 020-0693, Japan;1. Industrial Technology Research Institute, Zhengzhou university, Zhengzhou City, Henan, China;2. Department of automation, Tsinghua university, Beijing City, Beijing, China;1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singaporen;2. Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore;3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;4. Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, Japann;5. University 2020 Foundation, MA, USA;6. Department of Electronics and Communication Engineering, St. Joseph Engineering College, Mangalore, India;7. Department of Cardiology, National Heart Centre, Singapore |
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| Abstract: | Myocardial infarction (MI) was one of the most threatening cardiovascular diseases due to its suddenness and high mortality. Electrocardiography (ECG) reflected the electrophysiological activity of the heart which was widely used for the diagnosis of MI. The aim of the paper was to provide a novel method to detect MI leveraging ECG. Firstly, data enhancement technology was employed to extend the database and prevent overfitting. Then, principal component analysis (PCA) features, statistical features, and entropy features were computed as the representation of first layer features for each lead. Furthermore, the second layer features for each lead were extracted by using random forests (RF), and the feature extraction results were quantified as a classification data set. Finally, in order to evaluate the proposed method, two schemes for the intra-patient and inter-patient were employed. The accuracy, sensitivity, specificity and F1 values in the intra-patient scheme were 99.71%, 99.7%, 99.73%, and 99.71%, respectively, and 85.82%, 73.91%, 97.73%, and 83.9% in the inter-patient scheme. Meanwhile, compared with different methods including support vector machine (SVM), back propagation neural network (BPNN), and k-nearest neighbor (KNN), RF displayed the best performance. |
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| Keywords: | MI ECG Principal component analysis Statistical feature calculation Entropy calculation Random forests |
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