Automated characterization of cardiovascular diseases using wavelet transform features extracted from ECG signals

MOHSIN, Ahmad and FAUST, Oliver (2019). Automated characterization of cardiovascular diseases using wavelet transform features extracted from ECG signals. Journal of mechanics in medicine and biology, 19 (01), p. 1940009.

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Official URL: https://www.worldscientific.com/doi/10.1142/S02195...
Link to published version:: https://doi.org/10.1142/s0219519419400098

Abstract

Cardiovascular disease has been the leading cause of death worldwide. Electrocardiogram (ECG)-based heart disease diagnosis is simple, fast, cost effective and non-invasive. However, interpreting ECG waveforms can be taxing for a clinician who has to deal with hundreds of patients during a day. We propose computing machinery to reduce the workload of clinicians and to streamline the clinical work processes. Replacing human labor with machine work can lead to cost savings. Furthermore, it is possible to improve the diagnosis quality by reducing inter- and intra-observer variability. To support that claim, we created a computer program that recognizes normal, Dilated Cardiomyopathy (DCM), Hypertrophic Cardiomyopathy (HCM) or Myocardial Infarction (MI) ECG signals. The computer program combined Discrete Wavelet Transform (DWT) based feature extraction and K-Nearest Neighbor (K-NN) classification for discriminating the signal classes. The system was verified with tenfold cross validation based on labeled data from the PTB diagnostic ECG database. During the validation, we adjusted the number of neighbors k k for the machine learning algorithm. For k=3 k=3 , training set has an accuracy and cross validation of 98.33% and 95%, respectively. However, when k=5 k=5 , it showed constant for training set but dropped drastically to 80% for cross-validation. Hence, training set k=5 k=5 prevails. Furthermore, a confusion matrix proved that normal data was identified with 96.7% accuracy, 99.6% sensitivity and 99.4% specificity. This means an error of 3.3% will occur. For every 30 normal signals, the classifier will mislabel only 1 of the them as HCM. With these results, we are confident that the proposed system can improve the speed and accuracy with which normal and diseased subjects are identified. Diseased subjects can be treated earlier which improves their probability of survival.

Item Type: Article
Additional Information: ** From Crossref via Jisc Publications Router **Journal IDs: pissn 0219-5194; eissn 1793-6810 **History: published 08-02-2019
Uncontrolled Keywords: Biomedical Engineering
Identification Number: https://doi.org/10.1142/s0219519419400098
SWORD Depositor: Louise Beirne
Depositing User: Louise Beirne
Date Deposited: 21 Feb 2019 13:58
Last Modified: 13 Mar 2019 14:59
URI: http://shura.shu.ac.uk/id/eprint/24045

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