De-Noising of ECG Signals by Design of an Optimized Wavelet

Vahid Makhdoomi Kaviri,Saeid Marjani,Masoud Sabaghi

doi:10.4236/cs.2016.711314

Abstract

In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.

Highlights

There are many methods to de-noise a signal
Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the Signal-to-Noise Ratio (SNR) of about 2.5 dB
The proposed algorithm is applied on this noisy signal and results are compared with other wavelets de-noising from a SNR point of view

Summary

Introduction

There are many methods to de-noise a signal. De-noising is so important in signal processing, for biomedical signals. Discrete Wavelet Transform (DWT) is currently used in a wide variety of signal processing applications, such as audio and video compression, removal of noise in audio, and the simulation of wireless antenna distribution. Wavelet transform is one of the most powerful mathematical tools for digital signal processing [1], such as compression and de-noising. Genetic algorithm (GA) is an optimization technique based on the “survival of the fittest” [2] In this method, a wide range of inputs are checked and the best one of them is chosen. Discrete wavelet transform is implemented by digital FIR filters. The best coefficients of filters will be obtained using genetic algorithm. The paper is organized as follows: in Section 2, de-noising using wavelet implementation is presented.

Wavelet Based De-Noising

Wavelet Design Using Genetic Algorithm

Multi-Resolution Analysis for Design of an Appropriate Wavelet for De-Noising

GA Strategy for Design Optimum Coefficients to Minimize the SNR

Simulation Results

Conclusion