Arrhythmia Classification of ECG Signals Using Hybrid Features
Joint Authors
Anwar, Syed Muhammad
Majid, Muhammad
Alnowami, Majdi
Gul, Maheen
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-12
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions.
In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented.
Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features.
It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes.
RR interval information is used as a dynamic feature.
The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification.
Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features.
These hybrid features are combined and fed to a neural network to classify arrhythmia.
The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats.
The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.
American Psychological Association (APA)
Anwar, Syed Muhammad& Gul, Maheen& Majid, Muhammad& Alnowami, Majdi. 2018. Arrhythmia Classification of ECG Signals Using Hybrid Features. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1131779
Modern Language Association (MLA)
Anwar, Syed Muhammad…[et al.]. Arrhythmia Classification of ECG Signals Using Hybrid Features. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1131779
American Medical Association (AMA)
Anwar, Syed Muhammad& Gul, Maheen& Majid, Muhammad& Alnowami, Majdi. Arrhythmia Classification of ECG Signals Using Hybrid Features. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1131779
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131779