Atrial Fibrillation Identification through ECG Signals
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Date
2019Author
Ng Joe, Yee
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Chong Yen, Fook
Lim Chee, Chin
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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.