Denoising ECG by Adaptive Filter with Empirical Mode Decomposition

TL;DR

This paper introduces a novel adaptive filtering method combined with empirical mode decomposition to effectively denoise ECG signals contaminated by multiple noise types, improving signal quality for better cardiac diagnosis.

Contribution

The paper presents an innovative parallel EMD adaptive filter structure that enhances ECG denoising performance against various noise sources, outperforming existing methods.

Findings

Achieved significant SNR improvement on MIT-BIH database

Effectively removes multiple noise types simultaneously

Outperforms existing denoising techniques

Abstract

Electrocardiogram (ECG) signal is an important physiological signal which contains cardiac information and is the basis to diagnosis cardiac related diseases. In this paper, several innovative and efficient methods based on adaptive filter and empirical mode decomposition (EMD) to denoise ECG signal contaminated by various kinds of noise, including baseline wander (BW), power line interference (PLI), electrode motion artifact (EM) and muscle artifact (MA), are proposed. We first present a novel method based on EMD and adaptive filter for the removal of BW and PLI in ECG signal. We then extend the method to the complex scenario where four most common noises, PLI, BW, EM and MA are present. The proposed Parallel EMD adaptive filter structure yields the best SNR improvement on the MIT-BIH arrhythmia database, corrupted by the four types of noises.