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Based on wavelet and neural network ECG automatic analysis technique
Author: ZhengPeng
Tutor: ZhuCanYan
School: Suzhou University
Course: Communication and Information System
Keywords: ECG Wavelet Transform QRS detection Artificial Neural Networks
CLC: TN911.6
Type: Master's thesis
Year: 2010
Downloads: 194
Quote: 2
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Abstract
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In this thesis, the pretreatment of ECG waveform detection, feature extraction , automatic classification and ECG diagnosis of disease and other issues of a more comprehensive study , completed the automated ECG analysis algorithm design and implementation. First , this paper analyzes the ECG interference and for frequency interference, EMG noise such as noise and baseline drift take relevant denoising ; Additionally , using a wavelet-based ECG denoising algorithm, the wavelet decomposition and reconstruction method and wavelet thresholding method for the research, analysis of both the characteristics applied to ECG signal denoising and organic combination , can be seen through the simulation of the algorithm can remove the ECG -frequency interference , baseline wander , EMG noise and other major disturbances , and can better preserve the original characteristics of the ECG , with high practical value. Then, using the classical differential threshold method critical ECG QRS wave detection , and for the traditional limitations of the differential threshold method is proposed using two orthogonal quadratic B-spline wavelets to pinpoint the real-time detection of the QRS complex algorithms. The algorithm using wavelet transform and signal singularity relationships identified in 23 scale R wave in 21 scale for the beginning and end of QRS wave detection . Simulation results show that the algorithm for QRS wave average recognition rate of 99.64% , QRS complex capable of accurately detecting and locating . Finally , in the current ECG common testing standards , based on the design of a novel , including RR interval , R -wave peak , QRS interval , RS falling slope characteristic quantities such as automatic diagnosis neural network model , discussed the nerve network design and training process . The MIT-BIH arrhythmia database authentication standard , BP neural network based on LM algorithm not only can effectively detect the presence of ECG atrial and ventricular premature beats waveform , and can be based on the patient 's own characteristics ECG waveform parameters for differences threshold selection , for a variety of cardiac arrhythmias class can achieve satisfactory diagnostic results.
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