Accurate detection of myocardial infarction using non linear features with ECG signals
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ORIGINAL RESEARCH
Accurate detection of myocardial infarction using non linear features with ECG signals Chaitra Sridhar1 · Oh Shu Lih2 · V. Jahmunah2 · Joel E. W. Koh2 · Edward J. Ciaccio3 · Tan Ru San4 · N. Arunkumar5 · Seifedine Kadry6 · U. Rajendra Acharya2,7,8 Received: 11 March 2020 / Accepted: 5 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Interrupted blood flow to regions of the heart causes damage to heart muscles, resulting in myocardial infarction (MI). MI is a major source of death worldwide. Accurate and timely detection of MI facilitates initiation of emergency revascularization in acute MI and early secondary prevention therapy in established MI. In both acute and ambulatory settings, the electrocardiogram (ECG) is a standard data type for diagnosis. ECG abnormalities associated with MI can be subtle, and may escape detection upon clinical reading. Experience and training are required to visually extract salient information present in the ECG signals. This process of characterization is manually intensive, and prone to intra-and inter-observer-variability. The clinical problem can be posed as one of diagnostic classification of MI versus no MI on the ECG, which is amenable to computational solutions. Computer Aided Diagnosis (CAD) systems are designed to be automated, rapid, efficient, and ultimately cost-effective systems that can be employed to detect ECG abnormalities associated with MI. In this work, ECGs from 200 subjects were analyzed (52 normal and 148 MI). The proposed methodology involves pre-processing of signals and subsequent detection of R peaks using the Pan-Tompkins algorithm. Nonlinear features were extracted. The extracted features were ranked based on Student’s t-test and input to k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), and Decision Tree (DT) classifiers for distinguishing normal versus MI classes. This method yielded the highest accuracy 97.96%, sensitivity 98.89%, and specificity 93.80% using the SVM classifier. Keywords Myocardial infarction · Computer aided diagnostic system · Electrocardiogram · Pan Thompkins algorithm · Classifiers
1 Introduction * U. Rajendra Acharya [email protected] 1
Schiller Healthcare India Private Limited, Bangalore, India
2
School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3
Division of Cardiology, Department of Medicine, Columbia University, New York, USA
4
National Heart Centre, Singapore, Singapore
5
Biomedical Engineering Department, Rathinam Technical Campus, Coimbatore, India
6
Department of Mathematics and Computer Science, Beirut Arab University, Beirut 115020, Lebanon
7
Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
8
International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
The heart is an essential organ composed primarily of muscle tissue. The function of the coronary arteries is to supply oxygenated bl
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