Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning
- Authors
- Aamir, Khalid Mahmood; Ramzan, Muhammad; Skinadar, Saima; Khan, Hikmat Ullah; Tariq, Usman; Lee, Hyunsoo; Nam, Yunyoung; Khan, Muhammad Attique
- Issue Date
- Jan-2022
- Publisher
- Tech Science Press
- Keywords
- Heart disease; signals; preprocessing; detection; machine learning
- Citation
- Computers, Materials and Continua, v.71, no.1, pp 17 - 33
- Pages
- 17
- Journal Title
- Computers, Materials and Continua
- Volume
- 71
- Number
- 1
- Start Page
- 17
- End Page
- 33
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20360
- DOI
- 10.32604/cmc.2022.018613
- ISSN
- 1546-2218
1546-2226
- Abstract
- This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and premature ventricular contraction. Similarities of a given signal with both the templates are computed in the spectral domain. The empirical analysis reveals precisions for the detector and the applied classifier are 100% and 77.27%, respectively. Moreover, instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08-0.6 Hz. This indicates a serious loss of high-frequency contents in the spectrum, implying that the heart's overall activity is slowed down. This study may help medical practitioners in detecting the heart disease type based on signal analysis.
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Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
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