ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions

Abstract

The electrocardiogram signal (ECG) is a bio-signal used to determine cardiac health. However, different types of noise that commonly accompany these signals can hide valuable information for diagnosing disorders. The paper presents an experimental study to remove the noise in ECG signals using the Discrete Wavelet Transform (DWT) theory and a set of thresholds filters for efficient noise filtering. For the assessment process, we used ECG records from MIT-BIH Arrhythmia database (MITDB) and standardized noise signals (muscle activity and electrode-skin contact) database from the Noise Stress Test database. In addition to the ECG signals a white Gaussian noise present in electrical type signals was added. Furthermore, as a first step we considered baseline wander and power line interference reduction. The metrics used are the Signal-to-Noise Ratio (SNR), the Root Mean Squared Error (RMSE), the Percent Root mean square Difference (PRD), and the Euclidian L2 Norm standard (L2N). Results reveal that there is not a single combination of filtering thresholds (function and value) to minimize all types of noise and interference present in ECG signals. Reason why an ECG denoising algorithm is proposed which allows choosing the appropriate combination (function-value) threshold, where the SNR values were the maximum and the error values were the minimum.

Enhanced ECG Record Quality: Integrated Artifact Suppression Using Soft Threshold on Wavelet Coefficients and Adaptive Filter Model

“…Adaptive filters have been successfully applied in fields as diverse as communications, radar or biomedical engineering. Although the applications are quite different in nature, but they have one basic common feature: An input vector and a desired response are used to calculate the estimation error (which used to control the values of a set of adjustable filter coefficients) [22,23]. The adjustable coefficients can be in the many different forms depending on the filter construction used.…”

“…In the requirement to eliminate the artifact of the ECG signal, the adaptive filter model applied in interference suppression as shown in Figure 6 is used; this model will be coordinated with the wICA system to improve the artifact suppression efficiency. e=d-y (10) In this model, the primary signal serves as the desired response for the adaptive filter; a reference signal is employed as the input to the filter; the reference signal is derived from a sensor or a set of sensors located such that it supplies the primary signal in a way that the information-bearing signal component is weak or essentially undetectable [22].…”

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network

“…The discrete WT (which is applied in this study) pursues only the limited set of wavelets such as locations and scales, th [14]. The types of noises discussed earlier, i.e., baseline wanders and ence can be constricted by applying the denoising technique based on performed in [15,16]. The cleaned signal gives a better interpretation o the denoising approach, diverse wavelets from the families includin and Coif are practiced, enhancing the signals [17].…”

“…The discrete WT (which is applied in this study) on the other hand pursues only the limited set of wavelets such as locations and scales, thus, more efficiently [14]. The types of noises discussed earlier, i.e., baseline wanders and powerline interference can be constricted by applying the denoising technique based on discrete wavelet as performed in [15,16]. The cleaned signal gives a better interpretation of the ECG.…”