EcgScorer: An open source MATLAB toolbox for ECG signal quality assessment

Cardiovascular diseases claim over 17 million lives annually. Prevention involves adopting healthy habits and regular check-ups, ideally outside hospitals to reduce healthcare costs, leveraging telemedicine tools. However, diagnosing CVDs outside hospitals can be challenging due to noise interference in electrocardiograms (ECGs), necessitating the use of Signal Quality Assessment (SQA) systems. This paper presents a MATLAB toolbox for automated ECG Signal Quality Assessment, featuring a novel method. Furthermore, the toolbox can extract up to 37 Signal Quality Indices (SQIs), commonly used as features in machine learning-based SQA. Therefore, our software has the potential to facilitate the healthcare process, resulting in efficient and cost-effective cardiovascular care.

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Publication Details

Journal
SoftwareX
Published
2024-09-23
DOI
https://doi.org/10.1016/j.softx.2024.101900
Citations
2
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.80
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article

EcgScorer: An open source MATLAB toolbox for ECG signal quality assessment

Noura Alexendre, Ntsama Eloundou Pascal, Simo Thierry, Fotsing Kuetche
2 citations
SoftwareX
ECG Monitoring and Analysis
0.80
article

EcgScorer: An open source MATLAB toolbox for ECG signal quality assessment

Noura Alexendre, Ntsama Eloundou Pascal, Simo Thierry, Fotsing Kuetche
article en
2 citations

Abstract

Cardiovascular diseases claim over 17 million lives annually. Prevention involves adopting healthy habits and regular check-ups, ideally outside hospitals to reduce healthcare costs, leveraging telemedicine tools. However, diagnosing CVDs outside hospitals can be challenging due to noise interference in electrocardiograms (ECGs), necessitating the use of Signal Quality Assessment (SQA) systems. This paper presents a MATLAB toolbox for automated ECG Signal Quality Assessment, featuring a novel method. Furthermore, the toolbox can extract up to 37 Signal Quality Indices (SQIs), commonly used as features in machine learning-based SQA. Therefore, our software has the potential to facilitate the healthcare process, resulting in efficient and cost-effective cardiovascular care.

SoftwareXVol. 28
University of Ngaoundéré (CM)
Openalex Percentile: Top 24%
ECG Monitoring and Analysis
0.80
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