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Atrial Fibrillation Identification through ECG Signals

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Main Authors: Ng Joe, Yee, Vikneswaran, Vijean, Saidatul Ardeenawatie, Awang, Chong Yen, Fook, Lim Chee, Chin
Other Authors: vikneswaran@unimap.edu.my
Format: Article
Language:English
Published: IOP Publishing 2020
Subjects:
Atrial fibrillation
Electrocardiogram (ECG)
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69033
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spelling my.unimap-690332020-12-16T08:34:57Z Atrial Fibrillation Identification through ECG Signals Ng Joe, Yee Vikneswaran, Vijean Saidatul Ardeenawatie, Awang Chong Yen, Fook Lim Chee, Chin vikneswaran@unimap.edu.my Atrial fibrillation Electrocardiogram (ECG) Link to publisher's homepage at https://iopscience.iop.org/ This paper presents an algorithm formulated to identify the atrial fibrillation complications through electrocardiogram (ECG) signals. The ECG data for the study was retrieved from Physio Net which consists of normal, atrial fibrillation and other rhythms. The Discrete Wavelet Transform (DWT) was used to remove baseline wanders. Pan Tompkins algorithm was utilized to detect the P, Q, R, S and T peak and thus the ECG signals were segmented based on each cycle. The morphological features were extracted directly from the time-series while statistical features were extracted after Stockwell transform (S- transform) was applied to the data. Genetic Algorithm (GA) and reliefF algorithm have been applied separately to select the optimum features for classification purpose. Bagged Tree ensemble algorithm, Decision Tree and k-Nearest Neighbour (KNN) algorithm were used as classifiers to identify atrial fibrillation through ECG signals. The classification results with and without feature selection techniques are presented. Prior to the feature selection, Bagged Tree is the classifier best performing classifier with 86.50% of accuracy, 84.38% of sensitivity and 91.94% of specificity. After feature selection, all the three classifiers have almost the same performance which is nearly 100% of accuracy, sensitivity and specificity. This shows that the proposed combinations of algorithms are reliable and able to improve the identification rate of the normal, atrial fibrillation and other rhythms using lesser number of features. 2020-12-16T08:34:57Z 2020-12-16T08:34:57Z 2019 Article Journal of Physics: Conference Series, vol.1372, 2019, 6 pages 1742-6588 (print) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69033 1742-6596 (online) https://iopscience.iop.org/issue/1742-6596/1372/1 en IOP Publishing
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Atrial fibrillation
Electrocardiogram (ECG)
spellingShingle Atrial fibrillation
Electrocardiogram (ECG)
Ng Joe, Yee
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Chong Yen, Fook
Lim Chee, Chin
Atrial Fibrillation Identification through ECG Signals
description Link to publisher's homepage at https://iopscience.iop.org/
author2 vikneswaran@unimap.edu.my
author_facet vikneswaran@unimap.edu.my
Ng Joe, Yee
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Chong Yen, Fook
Lim Chee, Chin
format Article
author Ng Joe, Yee
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Chong Yen, Fook
Lim Chee, Chin
author_sort Ng Joe, Yee
title Atrial Fibrillation Identification through ECG Signals
title_short Atrial Fibrillation Identification through ECG Signals
title_full Atrial Fibrillation Identification through ECG Signals
title_fullStr Atrial Fibrillation Identification through ECG Signals
title_full_unstemmed Atrial Fibrillation Identification through ECG Signals
title_sort atrial fibrillation identification through ecg signals
publisher IOP Publishing
publishDate 2020
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69033
_version_ 1698698546560106496
score 13.214268

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