ArticleSensors (Basel, Switzerland)2019
Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Improving Signal Quality in Non-Contact Electrocardiography: Novel Strategy for Motion Artifact Reduction.Sensors (Basel, Switzerland) · 2026Article
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- Hybrid Filtering Compensation Algorithm for Suppressing Random Errors in MEMS Arrays.Micromachines · 2024Article
- A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT.Sensors (Basel, Switzerland) · 2024Article
- Prediction of Fetal Growth Restriction for Fetal Umbilical Arterial/Venous Blood Flow Index Evaluated by Ultrasonic Doppler under Intelligent Algorithm.Computational and mathematical methods in medicine · 2022Article
- Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction.Sensors (Basel, Switzerland) · 2021Article
- Novel Stable Capacitive Electrocardiogram Measurement System.Sensors (Basel, Switzerland) · 2021Article
- Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform.Sensors (Basel, Switzerland) · 2020Article
- Noise-Resistant CECG Using Novel Capacitive Electrodes.Sensors (Basel, Switzerland) · 2020Article
- MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient.Sensors (Basel, Switzerland) · 2019Article
- Mono-Component Feature Extraction for Condition Assessment in Civil Structures Using Empirical Wavelet Transform.Sensors (Basel, Switzerland) · 2019Article
- Comparison of Active Electrode Materials for Non-Contact ECG Measurement.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (
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Registered trials
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