Evidence map›Paper›PMID 39031328›Full record

ReviewMedical & biological engineering & computing2024

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.

Matin Khalili, Hamid GholamHosseini, Andrew Lowe, Matthew M Y Kuo

Abstract readReview
In one paragraph

Review in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Matin KhaliliInstitute of Biomedical Technologies, Auckland University of Technology, 6 St Paul St, Auckland, 1010, New Zealand. matin.khalili@autuni.ac.nz.ORCID http://orcid.org/0009-0006-8193-4519
Hamid GholamHosseini *Institute of Biomedical Technologies, Auckland University of Technology, 6 St Paul St, Auckland, 1010, New Zealand.
Andrew Lowe *Institute of Biomedical Technologies, Auckland University of Technology, 6 St Paul St, Auckland, 1010, New Zealand.
Matthew M Y Kuo *Department of Computer Science and Software Engineering, Auckland University of Technology, 6 St Paul St, Auckland, 1010, New Zealand.

Funding

Ministry of Business, Innovation and Employment AUTX1903
6 · The paper itself

Abstract

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.

Indexed as

ArtifactsElectrocardiographyElectrodesSignal Processing, Computer-AssistedElectric CapacitanceHumansMotionSignal-To-Noise RatioAdaptive filteringDigital signal processingECG capacitive electrodesElectrode-tissue impedanceMotion artifact

Identifiers

PMID39031328
PMCPMC11568998

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.