Evidence map›Paper›PMID 37066934›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2023

Morphological ECG subtraction method for removing ECG artifacts from diaphragm EMG.

Liang Guo, Zhi-Wei Li, Han Zhang, Shuang-Miao Li, Jian-Heng Zhang

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 1 institution in 1 country.

Liang GuoSchool of Electrical and Information Engineering, South China Normal University, Foshan, Guangdong, China.
Zhi-Wei LiSchool of Electrical and Information Engineering, South China Normal University, Foshan, Guangdong, China.
Han ZhangSchool of Electrical and Information Engineering, South China Normal University, Foshan, Guangdong, China.
Shuang-Miao LiSchool of Electrical and Information Engineering, South China Normal University, Foshan, Guangdong, China.
Jian-Heng ZhangThe First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
South China Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiaphragmatic electromyographic (EMGdi) is a helpful method to reflect the respiratory center's activity visually. However, the electrocardiogram (ECG) severely affected its weakness, limiting its use.

objectiveTo remove the ECG artifact from the EMGdi, we designed a Morphological ECG subtraction method (MES) based on three steps: 1) ECG localization, 2) morphological tracking, and 3) ECG subtractor.

methodsWe evaluated the MES method against the wavelet-based dual-threshold and stationary wavelet filters using visual and frequency-domain characteristics (median frequency and power ratio).

resultsThe results show that the MES method can preserve the features of the original diaphragm signal for both surface diaphragm signal (SEMGdi) and clinical collection of diaphragm signal (EMGdi_clinic), and it is more effective than the wavelet-based dual-threshold and stationary wavelet filtering methods.

conclusionThe MES method is more effective than other methods. This technique may improve respiratory monitoring and assisted ventilation in patients with respiratory diseases.

Indexed as

DiaphragmSignal Processing, Computer-AssistedElectrocardiographyElectromyographyHumansThoraxDiaphragm EMGECG contaminationmorphological subtractionwavelet filter

Identifiers

PMID37066934
PMCPMC10200147
OpenAlexW4365510375

What OpenQuestion holds

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Registered trials

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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.