ECG Signal Denoising Using Wavelet Thresholding Techniques in Human Stress Assessment

Abstract

In recent years, Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Human Computer Interface (HCI), stress and emotional states assessment, etc. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference, and high frequency noises during data acquisition. In order to retain the ECG signal morphology, several researches have adopted using different preprocessing methods. In this work, the stroop color word test based mental stress inducement have done and ECG signals are acquired from 10 female subjects in the age range of 20 years to 25 years. We have considered the Discrete Wavelet Transform (DWT) based wavelet denoising have incorporated using different thresholding techniques to remove three major sources of noises from the acquired ECG signals namely, power line interference, baseline wandering, and high frequency noises. Three wavelet functions ("db4", "coif5" and "sym7") and four different thresholding methods are used to denoise the noise in ECG signals. The experimental result shows the significant reduction of above considered noises and it retains the ECG signal morphology effectively. Four different performance measures were considered to select the appropriate wavelet function and thresholding rule for efficient noise removal methods such as, Signal to Interference Ratio (SIR), noise power, Percentage Root Mean Square Difference (PRD) and finally periodogramof Power Spectral Density (PSD). The experimental result shows the "coif5" wavelet andrigrsurethresholding rule is optimal for unknown Signal to Noise Ratio (SNR) in the real time ECG signals.

A Fuzzy Based Approach for Denoising of ECG Signal using Wavelet Transform

“…Universal thresholdingcan be used as an alternative to the useof minimax threshold. It is bigger in magnitude than the minimaxthreshold [14] . The value of the threshold is calculated as: T=sqrt(2*log(length(X))……… ………………….…”

“…In the previous researches authors worked on wavelet functions like daubechies, coiflet, symmlet on very few decomposition levels with very less thresholding rules. [19] P. Karthikeyan et. Al.…”

Forecasting performance of denoising signal by Wavelet and Fourier Transforms using SARIMA model

“…Nevertheless, the transforms give only the information for spectral in frequency domain thus neglects the information about the time domain ( [6]). However, the concept of multi resolution were introduced in WT to give the information for both time and frequency domain.…”

Performance Enhancement of DCT Based Speaker Recognition Using Wavelet De-Noising

“…Following are two mostly used methods to determine the threshold [9]:  For fixed thresholding based denoising, the threshold t may be computed using Equation (3), where σ = Median-of-coefficients/0.6745, and n =total coefficients :…”