Atrial Fibrillation Identification through ECG Signals

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 us...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1372 (1), p.12058
Main Authors: Yee, Ng Joe, Vijean, Vikneswaran, Awang, Saidatul Ardeenawatie, Fook, Chong Yen, Chin, Lim Chee
Format: Article
Language:English
Subjects:
Algorithms
Cardiac arrhythmia
Classifiers
Decision trees
Discrete Wavelet Transform
Electrocardiography
Feature extraction
Feature selection
Fibrillation
Genetic algorithms
K-nearest neighbors algorithm
Physics
Sensitivity
Signal classification
Wavelet transforms
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1372/1/012058