Detection of myocardial infarction using analysis of vectorcardiographic loops

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254725" target="_blank" >RIV/61989100:27240/24:10254725 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0263224123016585" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0263224123016585</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measurement.2023.114094" target="_blank" >10.1016/j.measurement.2023.114094</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of myocardial infarction using analysis of vectorcardiographic loops

Popis výsledku v původním jazyce

Vectorcardiography is an alternative form of ECG for measuring electrical activity of the heart. It achieves higher sensitivity and provides the cardiologist additional information that can contribute to early diagnosis. This study is focused on proposal of a methodology for the processing of directly measured and transformed VCG records by using Kors regression transformation. A total 16 VCG features were extracted, while 12 features showed relevant information based on the statistical analysis and the method of maximum relevance minimum redundancy. These features served as input to the LDA and decision trees classifiers, while LDA achieved the most accurate results with accuracy 91.5%, specificity 76.3% and sensitivity 94.8% for directly measured VCG and accuracy 90.9%, specificity 76.3% and sensitivity 94.0% for transformed VCG. We conclude that this proposed methodology and the results obtained from it can be beneficial for the early diagnosis of myocardial infarction within the framework of automated detection.

Název v anglickém jazyce

Detection of myocardial infarction using analysis of vectorcardiographic loops

Popis výsledku anglicky

Vectorcardiography is an alternative form of ECG for measuring electrical activity of the heart. It achieves higher sensitivity and provides the cardiologist additional information that can contribute to early diagnosis. This study is focused on proposal of a methodology for the processing of directly measured and transformed VCG records by using Kors regression transformation. A total 16 VCG features were extracted, while 12 features showed relevant information based on the statistical analysis and the method of maximum relevance minimum redundancy. These features served as input to the LDA and decision trees classifiers, while LDA achieved the most accurate results with accuracy 91.5%, specificity 76.3% and sensitivity 94.8% for directly measured VCG and accuracy 90.9%, specificity 76.3% and sensitivity 94.0% for transformed VCG. We conclude that this proposed methodology and the results obtained from it can be beneficial for the early diagnosis of myocardial infarction within the framework of automated detection.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20600 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Measurement

ISSN

0263-2241

e-ISSN

1873-412X

Svazek periodika

226

Číslo periodika v rámci svazku

28 February 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001158678200001

EID výsledku v databázi Scopus

2-s2.0-85181942185