Abstract
The evaluation of distortion diagnosis using Wavelet function for Electrocardiogram (ECG), Electroencephalogram (EEG) and Phonocardiography (PCG) is not novel. However, some of the technological and economic issues remain challenging. The work in this paper is focusing on the reduction of the noise interferences and analyzes different kinds of ECG signals. Furthermore, a physiological monitoring system with a programming model for the filtration of ECG is presented. Kaiser based Finite Impulse Response (FIR) filter is used for noise reduction and identification of R peaks based on Peak Detection Algorithm (PDA). Two approaches are implemented for detecting the R peaks; Amplitude Threshold Value (ATV) and Peak Prediction Technique (PPT). Daubechies wavelet transform is applied to analyze the ECG of driver under stress, arrhythmia and sudden cardiac arrest signals. From the obtained results, it was found that the PPT is an effective and efficient technique in detecting the R peaks compared to ATV.
Highlights
Nowadays, Computer aided ECG signal analysis has gained thrust and incredible amount of work were carried out using these technologies for heart diagnosis
It was found that the Peak Prediction Technique (PPT) is an effective and efficient technique in detecting the R peaks compared to Amplitude Threshold Value (ATV)
Kaiser Window incorporated with Finite Impulse Response (FIR) filter is proposed as a tool for noise diminution
Summary
Computer aided ECG signal analysis has gained thrust and incredible amount of work were carried out using these technologies for heart diagnosis. In the wavelet based algorithm, the ECG signal is de-noised by removing the corresponding wavelet coefficients at higher scales [2]. Small variations in normal and noise corrupted ECG signal can been extracted using wavelet function [9,10]. The series connection of Discrete Wavelet Transform, thresholding and Inverse Discrete Wavelet Transform can remove the noises and achieve high Signal-to-Noise ratios. Filtration is done prior to R peak detection to minimize the false detection rate It can be seen from the results that the designed filter removes noise effectively. The wavelet of the abnormal ECG signals is compared with the normal one The data for this analysis was collected from PhysioBank data base [15].
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