Evidence map›Paper›PMID 27222735›Full record

ArticleHealthcare technology letters2016

Diagnostic measure to quantify loss of clinical components in multi-lead electrocardiogram.

R K Tripathy, L N Sharma, S Dandapat

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Healthcare technology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

3 authors.

R K TripathyDepartment of Electronics and Electrical Engineering , Indian Institute of Technology Guwahati , Guwahati 781039 , India.ORCID 0000-0003-2517-3103
L N SharmaDepartment of Electronics and Electrical Engineering , Indian Institute of Technology Guwahati , Guwahati 781039 , India.
S DandapatDepartment of Electronics and Electrical Engineering , Indian Institute of Technology Guwahati , Guwahati 781039 , India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this Letter, a novel principal component (PC)-based diagnostic measure (PCDM) is proposed to quantify loss of clinical components in the multi-lead electrocardiogram (MECG) signals. The analysis of MECG shows that, the clinical components are captured in few PCs. The proposed diagnostic measure is defined as the sum of weighted percentage root mean square difference (PRD) between the PCs of original and processed MECG signals. The values of the weight depend on the clinical importance of PCs. The PCDM is tested over MECG enhancement and a novel MECG data reduction scheme. The proposed measure is compared with weighted diagnostic distortion, wavelet energy diagnostic distortion and PRD. The qualitative evaluation is performed using Spearman rank-order correlation coefficient (SROCC) and Pearson linear correlation coefficient. The simulation result demonstrates that the PCDM performs better to quantify loss of clinical components in MECG and shows a SROCC value of 0.9686 with subjective measure.

Indexed as

clinical componentselectrocardiographyMECG data reduction schemeMECG enhancementMECG signalsmedical signal processingmultilead electrocardiogram signalsPearson linear correlation coefficientprincipal component analysisprincipal component-based diagnostic measureSpearman rank-order correlation coefficientwavelet energy diagnostic distortionweighted diagnostic distortionweighted percentage root mean square difference

Identifiers

PMID27222735
PMCPMC4814854

What OpenQuestion holds

Textmetadata
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.