Motion Artifact Reduction From Non-Contact ECG Recording Systems
type: CreativeWork
description: With the growing demand for real-time, long-term, and remote electrocardiogram (ECG) monitoring, non-contact capacitive sensors have emerged as a compelling solution due to their safety, comfort, and suitability for continuous use. However, a significant challenge remains; motion artifacts (MAs) caused by body movements and physiological vibrations. Such artifacts can significantly compromise diagnostic accuracy and may even lead to misinterpretations of cardiovascular conditions. To address this, this PhD research begins by demonstrating the feasibility of capacitive ECG acquisition from the head area to broaden its application scope, then transitions to a torso-worn chest strap system for practical implementation and MA reduction. It also includes a structured review of MAs in capacitive ECG systems, analyzing their mechanisms and mitigation strategies, with focused attention on electrode-tissue impedance (ETI)-based reference signal techniques. To further this development, the study tackles this challenge by developing novel techniques to reduce MAs and improve the quality of capacitive ECG signals. The Adaptive Noise Cancellation (ANC) technique is selected for MA mitigation, using a motion-correlated Reference Input Signal (RIS) and an Adaptive Filter (AF) to estimate and subtract MAs. A full-stack capacitive single-lead system is designed, spanning hardware, analog front-end design, and advanced signal processing. In this system, ECG and a highly correlated ETI-based RIS are simultaneously acquired through an efficient voltage injection network, offering improved performance compared to previous studies. In the first phase, the RIS is fed to a Recursive Least Squares adaptive filter to suppress MAs. Signal enhancement is evaluated using root mean square (RMS) reduction, standard deviation (STD) reduction, and signal-to-noise ratio (SNR) per peak. The system demonstrates strong performance, achieving RMS, STD, and per-peak SNR improvements of 17.9 dB (650.2%), …
abstract: With the growing demand for real-time, long-term, and remote electrocardiogram (ECG) monitoring, non-contact capacitive sensors have emerged as a compelling solution due to their safety, comfort, and suitability for continuous use. However, a significant challenge remains; motion artifacts (MAs) caused by body movements and physiological vibrations. Such artifacts can significantly compromise diagnostic accuracy and may even lead to misinterpretations of cardiovascular conditions. To address this, this PhD research begins by demonstrating the feasibility of capacitive ECG acquisition from the head area to broaden its application scope, then transitions to a torso-worn chest strap system for practical implementation and MA reduction. It also includes a structured review of MAs in capacitive ECG systems, analyzing their mechanisms and mitigation strategies, with focused attention on electrode-tissue impedance (ETI)-based reference signal techniques. To further this development, the study tackles this challenge by developing novel techniques to reduce MAs and improve the quality of capacitive ECG signals. The Adaptive Noise Cancellation (ANC) technique is selected for MA mitigation, using a motion-correlated Reference Input Signal (RIS) and an Adaptive Filter (AF) to estimate and subtract MAs. A full-stack capacitive single-lead system is designed, spanning hardware, analog front-end design, and advanced signal processing. In this system, ECG and a highly correlated ETI-based RIS are simultaneously acquired through an efficient voltage injection network, offering improved performance compared to previous studies. In the first phase, the RIS is fed to a Recursive Least Squares adaptive filter to suppress MAs. Signal enhancement is evaluated using root mean square (RMS) reduction, standard deviation (STD) reduction, and signal-to-noise ratio (SNR) per peak. The system demonstrates strong performance, achieving RMS, STD, and per-peak SNR improvements of 17.9 dB (650.2%), …
author: Khalili, Matin
date published: 2025
date modified: 2026-07-27T18:45:50Z
provider: AUT University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: AUT University