Noise Reduction in ECG Signal Using an Effective Hybrid Scheme

Pingping Bing,Zhihua Zhang,Zhong Wang,Wei Liu

doi:10.1109/access.2020.3021068

Abstract

Electrocardiogram (ECG) is a critical biological signal, which usually carries a great deal of essential information about patients. The high quality ECG signals are always required for a proper diagnosis of cardiac disorders. However, the raw ECG signals are highly noisy in nature. In the paper, we propose a hybrid denoising scheme to enhance ECG signals by combining high-order synchrosqueezing transform (FSSTH) with non-local means (NLM). With this method, a noisy ECG signal is first decomposed into an ensemble of intrinsic mode functions (IMFs) by FSSTH. Then, some noise is removed by eliminating a set of noisy IMFs that are determined by a scaling exponent obtained by the detrended fluctuation analysis (DFA); while the remaining IMFs are filtered by NLM. Finally, the denoised ECG signal is obtained by reconstructing the processed IMFs. Experiments are carried out using the simulated ECG signals and real ones from the MIT-BIH database, and the denoising performances are evaluated in terms of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). Results show that the hybrid denoising scheme involving both FSSTH and NLM is able to suppress complex noise from ECG signals more effectively while preserving the details well.

Highlights

The electrocardiogram (ECG) signals, which have been widely used to diagnose early-stage cardiovascular disorders, could reflect the electrical activity of heart and provide the clinical information about heart [1]–[4]
In this paper, inspired by the advantage of FSSTH, we propose a hybrid denoising scheme for the ECG signal based on high-order synchrosqueezing transform and non-local means (NLM)
2) Decompose the noisy ECG signal using the FSSTH and obtain an ensemble of band-limited intrinsic mode functions (IMFs). 3) Estimate the scaling exponent with respect to each IMF by the detrended fluctuation analysis (DFA) to determine the number of IMFs from FSSTH. 4) Evaluate each IMF via the DFA and determine the threshold

Summary

INTRODUCTION

The electrocardiogram (ECG) signals, which have been widely used to diagnose early-stage cardiovascular disorders, could reflect the electrical activity of heart and provide the clinical information about heart [1]–[4]. Pham and Meignen (2017) [26] developed a new adaptive signal analysis algorithm, which is termed as high-order synchrosqueezing transform (FSSTH) It is a new generalization of the STFT-based synchrosqueezing transform by computing more accurate estimates of the instantaneous frequencies using higher order approximations for both the amplitude and phase, which results in perfect concentration and reconstruction for a wider variety of signals [27]. HIGH-ORDER SYNCHROSQUEEZING TRANSFORM The high-order synchrosqueezing transform (FSSTH) is a new extension of the conventional STFT-based SST (FSST), VOLUME 8, 2020 which was first proposed by Thakur and Wu [32] It achieves more accurate estimates of the instantaneous frequencies by using higher order approximations for both the amplitude and phase [26], resulting in a perfect concentration and reconstruction for a wider range of AM-FM signal modes. FSSTH is more suitable for coping with fast frequency-varying signal compared to FSST

NON-LOCAL MEANS

DETRENDED FLUCTUATION ANALYSIS

EVALUATION ON SIMULATED ECG SIGNAL

DISCUSSIONS

CONCLUSION