Monte Carlo Filter-Based Motion Artifact Removal From Electrocardiogram Signal for Real-Time Telecardiology System

Abstract

Motion artifact (MA) contamination with electrocardiogram (ECG) signal is a common issue caused by body movement or sensor loosening, resulting in distortion of clinical features of ECG. In this work, the Monte Carlo filter (MCF)-based MA removal from single-channel ECG signal is proposed, assisting in real-time telecardiology systems. Initially, after R-peak detection and beat extraction, principal component (PC) analysis was performed upon clean ECG beats, and PC, with the highest energy, was assumed to be the feature beat. Using this feature beat, MA corrupted beats were denoised successively to achieve a clean pattern of ECG using MCF. A new approach of weight calculation and resampling was also proposed for better performance of the MCF. Performance of the proposed algorithm was tested on the IEEE Signal Processing Cup Challenge 2015 ECG database and MIT-BIH arrythmia records, with an improvement of signal-to-noise ratio between 10 and 15 dB, after MA removal. The proposed work was also tested on real-time ECG data collected from ten healthy volunteers using the AD8232 ECG module and Raspberry Pi, resulting in correlation coefficient higher than 0.99, between the original and denoised signals. The proposed algorithm was able to remove MA from any single-channel MA corrupted ECG signal, irrespective of lead category, using features of clean beats. A comparative study of the obtained result with previously published works ensured the superior performance on MA removal from ECG in the proposed work, along with real-time data collection, processing, and transmission.