Evidence map›Paper›PMID 34206512›Full record

ArticleSensors (Basel, Switzerland)2021

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering.

Yuki Nakano, Essam A Rashed, Tatsuhito Nakane, Ilkka Laakso, Akimasa Hirata

Open access · goldAbstract read
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

5 authors at 3 institutions in 3 countries.

Yuki NakanoDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
Essam A RashedDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.ORCID 0000-0001-6571-9807
Tatsuhito NakaneDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
Ilkka LaaksoDepartment of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland.
Akimasa HirataDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.ORCID 0000-0001-8336-1140
Nagoya Institute of Technology · JPAalto University · FISuez Canal University · EG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.

Indexed as

AlgorithmsSignal Processing, Computer-AssistedComputer SimulationElectrocardiographyHumansSignal-To-Noise Ratiocardiac source localizationelectrocardiographyfinite difference methodsinverse problems

Identifiers

PMID34206512
PMCPMC8271910
OpenAlexW3174604300

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.