Dissertation/Thesis Abstract

Wavelet transform and neural network
by Ghafoori, Elyar, M.S., California State University, Long Beach, 2014, 60; 1527935
Abstract (Summary)

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.

Indexing (document details)
Advisor: Moussavi, Maryam
Commitee:
School: California State University, Long Beach
School Location: United States -- California
Source: MAI 53/01M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Biomedical engineering, Electrical engineering, Computer science
Keywords: Atrial fibrillation, Bayesian classification, Biomedical, Digital signal processing, Neural networks, Stationary wavelet transform
Publication Number: 1527935
ISBN: 9781303984594
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest