ArticleSensors (Basel, Switzerland)2021
An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Hybrid Multi-Domain ECG Feature Learning with mRMR and CNN-Transformer for Cardiac Disease Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Wearable Electrocardiogram Technologies for the Early Detection of Acute Coronary Syndromes.JACC. Asia · 2026Review
- High-Reliability Signal Quality Validation for Biosignals Using Sensor Fusion and Software Indices.Sensors (Basel, Switzerland) · 2026Article
- Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today's wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient's daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.