ECG signal denoising by fractional wavelet transform thresholding

Abstract

Introduction The analysis of electrocardiogram (ECG) signals allows experts to diagnose several cardiac disorders. However, the accuracy of such diagnosis depends heavily on the signal quality. In this paper, an efficient method based on fractional wavelet decomposition coupled with thresholding techniques is proposed for noise removal. Methods The usual low-pass and high-pass filters of the wavelet transform are replaced by fractional-order ones. Thus, fractional wavelets are proposed, simulated, and compared to other wavelets for ECG denoising. The denoising process was made operational by the means of an appropriate choice of the wavelet transform coefficient thresholding and the wavelet decomposition level of the signal. Results Considering the relative error metrics, the best wavelet function for efficient denoising is the fractional one. In our study, we have used eight real ECG signals from the Physionet MITBIH. In order to prove the effectiveness of our method, we investigated the filtering of two types of noises, namely Gaussian white noise and power-line interference (PLI) noise. The proposed method removed the Gaussian white noise completely and had better performance on the PLI noise. Considering classical metrics of assessment, results show the advantage of the proposed method compared to other types of wavelets. Conclusion The proposed method is the most suitable one for removing PLI and Gaussian white noise from ECG signals with superior performance than other wavelets. Also, it can be applied for high-frequency denoising even without a priori frequency knowledge.

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

“…Pre-processing is vital part as the raw ECG signal contains the noises and artifacts that may lead to misclassification. We reviewed some recent ECG signal pre-processing techniques [19][20][21][22][23][24][25]. The empirical model decomposition filtering technique had designed [19] to denoise ECG signals.…”

“…The processing of such technique was divided into two phases as 1D U-net had designed to denoise ECG signal and 1D DR-NET model had designed to reconstruct ECG signal followed by the waveform distortion correction. Another contemporary ECG signal denoising approach, fractional wavelet decomposition, was investigated in [25]. They created thresholding to reduce noise.…”

“…As ECG signals are vulnerable to different types of noises, a robust and effective ECG denoising technique is required before applying automatic feature extraction and classification. The transform domain and deep learning-based ECG signal denoising techniques [19][20][21][22][23][24][25] suffer from high processing time with the possibility of original heartbeats-related data loss.…”

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

“…Moreover, these methods have been applied to the extraction of a noise-free signal from a noisy ECG signal. The scientific community has intensively investigated ECG signal processing in the biomedical field, notably its denoising for trustworthy diagnosis [4]. Several denoising strategies have been investigated in the signal processing literature.…”

Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier

“…The discrete wavelet transform (DWT) is a very powerful tool in the field of signal processing [17]- [20], this technique gives satisfying results in the processing of the noise which affects the signal that can allows us to reconstruct a denoised signal, also the DWT allows to extract different features that characterize the signal. The interest of the discrete wavelet transforms (DWT) pushes us to better exploit this technique in the processing of ECG signal [21], [22]. Also, machine learning techniques are constantly evolving; this evolution is reflected in the use of these techniques in several areas [23], more precisely in the classification and identification of signals.…”