Accurate detection of myocardial infarction using non linear features with ECG signals
Article
Sridhar, Chaitra, Lih, Oh Shu, Jahmunah, V., Koh, Joel E. W., Ciaccio, Edward J., San, Tan Ru, Arunkumar, N., Kadry, Seifedine and Acharya, U. Rajendra. 2021. "Accurate detection of myocardial infarction using non linear features with ECG signals." Journal of Ambient Intelligence and Humanized Computing. 12 (3), pp. 3227-3244. https://doi.org/10.1007/s12652-020-02536-4
Article Title | Accurate detection of myocardial infarction using non linear features with ECG signals |
---|---|
ERA Journal ID | 42085 |
Article Category | Article |
Authors | Sridhar, Chaitra, Lih, Oh Shu, Jahmunah, V., Koh, Joel E. W., Ciaccio, Edward J., San, Tan Ru, Arunkumar, N., Kadry, Seifedine and Acharya, U. Rajendra |
Journal Title | Journal of Ambient Intelligence and Humanized Computing |
Journal Citation | 12 (3), pp. 3227-3244 |
Number of Pages | 18 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1868-5137 |
1868-5145 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s12652-020-02536-4 |
Web Address (URL) | https://link.springer.com/article/10.1007/s12652-020-02536-4 |
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 | Classifiers; nearest neighbor (KNN); Myocardial infarction ; Computer aided diagnostic system; Electrocardiogram; Pan Thompkins algorithm |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Schiller Healthcare India, India |
Ngee Ann Polytechnic, Singapore | |
Columbia University, United States | |
National Heart Centre, Singapore | |
Biomedical Engineering Department, India | |
Beirut Arab University, Lebanon | |
Asia University, Taiwan | |
Kumamoto University, Japan |
Permalink -
https://research.usq.edu.au/item/z1w2y/accurate-detection-of-myocardial-infarction-using-non-linear-features-with-ecg-signals
86
total views0
total downloads1
views this month0
downloads this month