Wavelet Scattering Transform for ECG Beat Classification
Joint Authors
Liu, Zhishuai
Yao, Guihua
Zhang, Qing
Zhang, Junpu
Zeng, Xueying
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia.
However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity.
The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators.
We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats.
In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat.
Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows.
These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification.
The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation.
Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.
American Psychological Association (APA)
Liu, Zhishuai& Yao, Guihua& Zhang, Qing& Zhang, Junpu& Zeng, Xueying. 2020. Wavelet Scattering Transform for ECG Beat Classification. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139390
Modern Language Association (MLA)
Liu, Zhishuai…[et al.]. Wavelet Scattering Transform for ECG Beat Classification. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139390
American Medical Association (AMA)
Liu, Zhishuai& Yao, Guihua& Zhang, Qing& Zhang, Junpu& Zeng, Xueying. Wavelet Scattering Transform for ECG Beat Classification. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139390
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139390