Evidence map›Paper›PMID 29966231›Full record

ArticleSensors (Basel, Switzerland)2018

Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors.

Minseok Seo, Minho Choi, Jun Seong Lee, Sang Woo Kim

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
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

7 citing papers in PubMed, 15 citations in OpenAlex.

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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 at 1 institution in 1 country.

Minseok SeoDepartment of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea. seomseok@postech.edu.ORCID 0000-0002-8711-8428
Minho ChoiDepartment of Creative IT Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea. minho17@postech.edu.ORCID 0000-0002-9162-9319
Jun Seong LeeDepartment of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea. junsunglee@postech.edu.
Sang Woo KimDepartment of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea. swkim@postech.edu.
Pohang University of Science and Technology · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.

Indexed as

AlgorithmsArtifactsElectrocardiographyHumansMotionSignal Processing, Computer-AssistedSignal-To-Noise Ratioactive noise cancellationaffine projection sign algorithmmotion artifactsnoisy signalQRS intervalSNR

Identifiers

PMID29966231
PMCPMC6069047
OpenAlexW2809811813

What OpenQuestion holds

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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.