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• W2799962034 abstract "Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT. We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis." @default.
• W2799962034 created "2018-05-17" @default.
• W2799962034 creator A5030314469 @default.
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• W2799962034 date "2018-01-01" @default.
• W2799962034 modified "2023-10-15" @default.
• W2799962034 title "Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform" @default.
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• W2799962034 doi "https://doi.org/10.1016/j.bbe.2018.04.004" @default.
• W2799962034 hasPublicationYear "2018" @default.
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