Abstract
Atrial fibrillation (AFib) is a type of heart arrhythmia, marked by an erratic and rapid contraction of the atria. Computer-aided diagnosis of AFib using electrocardiogram (ECG) sensor data may become a valuable tool in the detection and management of this common cardiac arrhythmia. In this letter, we present a new hybrid approach for the automatic classification of ECG signals using the local mean decomposition (LMD) and ensemble boosted trees classifier (EBTC). The LMD algorithm is employed to adaptively decompose the recorded ECG data into product functions (PFs). Four entropy-based features namely, log energy, sure, Shannon, and threshold entropy are computed from each PFs. The Kruskal-Wallis algorithm is employed to check the statistical significance of the obtained features and an EBTC is used for the screening of AFib episodes. The proposed technique achieved the highest classification accuracy of 92.33%, 90.33%, and 90.00% by classifying immediate terminating and non-terminating, terminates after one minute and non-terminating, immediate terminating and terminates after one minute AFib episodes, respectively. The presented method outperforms the existing machine learning-based approaches for detecting AFib using ECG data acquired from the publicly accessible AF Termination Challenge Database.
Published Version
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