Early Detection of Heart Valve Disease Employing Multiclass Classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134093" target="_blank" >RIV/00216305:26220/19:PU134093 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8970870" target="_blank" >https://ieeexplore.ieee.org/document/8970870</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970870" target="_blank" >10.1109/ICUMT48472.2019.8970870</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Early Detection of Heart Valve Disease Employing Multiclass Classifier

Popis výsledku v původním jazyce

Cardiac disorder can prove to be fatal for a person’s life. Therefore, these disorders must be detected precisely in the preliminary stages. By the use of cardiac auscultation examination, one can examine the heart sounds. Cardiovascular auscultation is the most widely used technique to listen and analyze the cardiac sound in the form of phonocardiogram using an electronic stethoscope. Useful information can be derived from the PCG signal to derive the accurate functioning and status of the heart. Based on information derived, the heart sound signal can be classified into multiple categories. This method proposes an automatic, real-time and modified classification over previous methods to detect cardiac disorder by PCG heart sound signal and was tested over a database containing 5 categories of heart sound signal (PCG signals) which contains signals of one normal and 4 are abnormal categories. The method achieved an accuracy of 97.50 % during the classification process. Features are extracted from the phonocardiogram signal and then those signals are processed using machine learning classification techniques. The experimental observations suggest that the proposed model is efficient for classification of the multi-class heart sounds.

Název v anglickém jazyce

Early Detection of Heart Valve Disease Employing Multiclass Classifier

Popis výsledku anglicky

Cardiac disorder can prove to be fatal for a person’s life. Therefore, these disorders must be detected precisely in the preliminary stages. By the use of cardiac auscultation examination, one can examine the heart sounds. Cardiovascular auscultation is the most widely used technique to listen and analyze the cardiac sound in the form of phonocardiogram using an electronic stethoscope. Useful information can be derived from the PCG signal to derive the accurate functioning and status of the heart. Based on information derived, the heart sound signal can be classified into multiple categories. This method proposes an automatic, real-time and modified classification over previous methods to detect cardiac disorder by PCG heart sound signal and was tested over a database containing 5 categories of heart sound signal (PCG signals) which contains signals of one normal and 4 are abnormal categories. The method achieved an accuracy of 97.50 % during the classification process. Features are extracted from the phonocardiogram signal and then those signals are processed using machine learning classification techniques. The experimental observations suggest that the proposed model is efficient for classification of the multi-class heart sounds.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

978-1-7281-5764-1

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Neuveden

Místo vydání

Dublin, Ireland

Místo konání akce

Dublin

Datum konání akce

28. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000540651700041