Analysis of ECG Signal Denoising Algorithms in DWT and EEMD Domains

Abstract

Accurate analysis of ECG signals becomes difficult when a lot of noise such as AC (Power line) Interference, Electromyogram (EMG), Baseline wandering, channel noise, electrode motion, motion artifact, Gaussian noise & high frequency noise based on the frequency variation are present in the ECG signal. Thus, for better analysis and characterization of ECG, noise removal becomes an essential part. Denoising of ECG signals plays a very important role in diagnosis and detection of various cardiovascular diseases. The various methods available for denoising of ECG signals include linear filtering, Empirical Mode Decomposition (EMD), Independent and Principal Component Analysis, Neural networks, adaptive filtering etc. In recent studies by several researchers compared to the above mentioned denoising methods Discrete Wavelet Transform (DWT) and Ensemble Empirical Mode reducing noise from ECG signal. This paper presents the performance analysis on ECG denoising algorithms in EEMD and wavelet domains by evaluating Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) in order to compare the effectiveness of these two methods in reducing the noise. 

Emd and Wavelet-Based Ecg Signal Denoising and QRS Complex Detection

“…A method combining EMD with a windowing technique to filter noise from initial IMFs while preserving the QRS complex, followed by adaptive wavelet thresholding [7]An adaptive Bayesian wavelet shrinkage approach for filtering high-resolution ECGs, involving wavelet transform, Bayesian coefficient shrinkage, and reconstruction to effectively preserve high-frequency QRS components [8].The discrete wavelet transform with various wavelets and thresholding techniques to denoise ECG signals for heart disease applications, identifying the optimal wavelet ("coif5") and thresholding rule ("rigsure") for real-time denoising [9] A hybrid genetic algorithm and wavelet transform approach for ECG denoising, using the GA to optimize wavelet parameters for maximizing non-stationary noise reduction and producing high-quality clinical ECG signals [10].A new adaptive filtering scheme based on inter-beat averaging algorithms to enhance ECG signals during effort tests, providing a better output with simplified hardware requirements of a single recording channel [11].An ECG feature extraction algorithm using the Daubechies 4 wavelet, selected for its similarity to the ECG waveform, successfully detecting and extracting primary ECG features with less than 10% error [12]. Compares discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) methods for ECG denoising using signal-to-noise ratio and root mean square error metrics, highlighting their advantages over other techniques [13].Categorizes and evaluates state-of-the-art ECG denoising techniques across different noise types using benchmark databases, identifying notable methods like wavelet, EMD, and deep learning approaches [14]. An efficient compressed sensing approach for ECG denoising utilizing basis pursuit with low-pass filtering and ADMM optimization to remove baseline wander and Gaussian noise while preserving signal details [15].…”

Classification of Breast Cancer Using Ensemble Empirical Mode Decomposition and Autoencoder-Based Methods

“…This method [13] proposed an interactive thresholding technique that selected gray-level thresholds from which the breast and dense tissue regions in the breast were identified. Then it calculates the density ratio of the radiograph from the histogram of the image [14][15][16][17].…”

“…After calculating all the IMFs, we can reach the initial signal by summing all of them with each other and the final remainder. Hilbert-Huang transformation is used to display IMFs in time-frequency space [15]. In the Hilbert-Huang transformation, the Hilbert transform is applied to the IMFs obtained from EMD.…”

Design of a secured telehealth system based on multiple biosignals diagnosis and classification for IoT application

“…The first step for data preparation is to filter (Bhardwaj et al, 2016;Qureshi et al, 2017) ECG signals. The ECG signal is reached by different noises during the acquisition, which correspond to harmonics in the power supply networks, despite the presence of various analogue filters contained on the e-Health shield as shown in Figure 9.…”