ECG Signal De-Noising and Feature Extraction using Discrete Wavelet Transform
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : Raaed Faleh Hassan, Sally Abdulmunem Shaker
|DOI : 10.14445/22315381/IJETT-V63P206|
MLA Style: Raaed Faleh Hassan, Sally Abdulmunem Shaker"ECG Signal De-Noising and Feature Extraction using Discrete Wavelet Transform" International Journal of Engineering Trends and Technology 63.1 (2018): 32-39.
APA Style:Raaed Faleh Hassan, Sally Abdulmunem Shaker (2018). ECG Signal De-Noising and Feature Extraction using Discrete Wavelet Transform. International Journal of Engineering Trends and Technology, 63(1), 32-39.
Electrocardiogram (ECG) provides an important information about cardiovascular performance, it is a noninvasive technique utilized as a main diagnostic appliance for cardiovascular diseases. The clear ECG signal supply valuable information about the electrophysiology of the heart diseases and ischemic changes that may happen. This research aims to extract the common features of the ECG signals based on Discrete Wavelet Transform(DWT). As the ECG signal suffers from two types of interferences: power line interference and baseline wander interference, therefore DWT has used firstly for de-noising the ECG signal then extracting its features. Daubechies 4 (db4) for R wave detection is employed, because R wave has high amplitude so it is easily detected and used as a reference point to detect the other waves by creating a window for each wave and search in minima and maxima amplitude, then extract other feature R-R, P-R interval, QRS width, heart rate and ST Deviation. The analysis of ECG signal is executed in MATLAB environment. This work is tested with signals imported from physionet and use field MIT-BIH Arrhythmia database and PTB database. The collected results appear a sensitivity of 99.6% and a positive predictivity of 100% for QRS complex wave detection, the P peaks detection show sensitivity of 97.9% and a positive predictivity of 98.1% and T peaks detection show sensitivity of 97.8% and a positive predictivity of 98.6%.
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ECG signal, MATLAB, DWT, QRS, Daubechies, db4.