ArticleSensors (Basel, Switzerland)2020
Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Self-Maintaining Electrode-Skin Interface for Motion-Robust Biosignal Monitoring.Advanced healthcare materials · 2026Article
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- Monitoring military pilots with textile sensors: physiological responses and signal quality under extreme conditions.Frontiers in physiology · 2026Article
- Introduction to electrocardiogram signal quality assessment and estimated accuracy for textile electrodes.Scientific reports · 2025Article
- Augmenting Circadian Biology Research With Data Science.Journal of biological rhythms · 2025Review
- Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.Medical & biological engineering & computing · 2024Review
- DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal.IEEE journal of biomedical and health informatics · 2024Article
- HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.Sensors (Basel, Switzerland) · 2024Article
- A method for noninvasive beat-by-beat visualization of His bundle signals.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Motion Artifact Reduction in Electrocardiogram Signals Through a Redundant Denoising Independent Component Analysis Method for Wearable Health Care Monitoring Systems: Algorithm Development and Validation.JMIR medical informatics · 2022Article
- Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal.Sensors (Basel, Switzerland) · 2022Article
- Lessons from a multicenter clinical trial with an approved wearable electrocardiogram: issues and practical considerations.Translational and clinical pharmacology · 2022Article
- Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms.Sensors (Basel, Switzerland) · 2022Review
- An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2021Article
- Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.Healthcare technology letters · 2021Article
- Human Body-Electrode Interfaces for Wide-Frequency Sensing and Communication: A Review.Nanomaterials (Basel, Switzerland) · 2021Review
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.
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Registered trials
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