Automatic and accurate detection of Atrial Fibrillation (AF) from the noninvasive ECG signal is imperative in Electrocardiography. AF is mainly reflected in the ECG signal with the absence of P wave and/or irregular RR intervals. Signal processing tools can assess such detailed changes in the ECG, leading to an accurate diagnosis of AF. The proposed method relies on proper noise filtering, Stationary Wavelet Transform, and signal Power Spectrum Estimation. A feature extraction technique and a Neural Network classifier have been employed to determine the presence and absence of the AF episodes. Implementation of the proposed method with 5-fold cross validation on more than 230 hours of ECG data from MIT-BIH arterial fibrillation annotated database demonstrated an accuracy of 93% in classification of the AF and normal ECG signals.
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 53/01M(E), Masters Abstracts International|
|Subjects:||Biomedical engineering, Electrical engineering, Computer science|
|Keywords:||Atrial fibrillation, Bayesian classification, Biomedical, Digital signal processing, Neural networks, Stationary wavelet transform|
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