Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50015698" target="_blank" >RIV/62690094:18450/19:50015698 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10489-019-01461-0" target="_blank" >https://link.springer.com/article/10.1007/s10489-019-01461-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10489-019-01461-0" target="_blank" >10.1007/s10489-019-01461-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

Popis výsledku v původním jazyce

Arrhythmia is a disease-influencing heart and is manifested by an irregular heartbeat. Atrial fibrillation (A(fib)), atrial flutter (A(fl)), and ventricular fibrillation (V-fib) are heart arrhythmias affecting predominantly senior citizens. An electrocardiogram (ECG) is a device serving to measure the ECG signal and diagnosis of an abnormal pattern which represents a heartbeat defects. Though it is possible to analyze these signals manually, in some cases it is a difficult task due to the often signal distortion by noise. Furthermore, manual analyzation of patterns is subjective and can lead to an inaccurate diagnosis. An automated computer-aided diagnosis (CAD) is a technique to eliminate these shortcomings. In this work, we present an 6-layer deep convolutional neural network (CNN) for automatic ECG pattern classification of the normal (N-r), A(fib), A(fl), and V-fib classes. This proposed CNN model requires simple feature extraction and no pre-processing of ECG signals. For two seconds ECG segments, the model obtained the accuracy of 97.78%, specificity and sensitivity of 98.82% and 99.76% respectively. This proposed system can be used as an assistant automatic tool in a clinical environment as a decision support system.

Název v anglickém jazyce

Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

Popis výsledku anglicky

Arrhythmia is a disease-influencing heart and is manifested by an irregular heartbeat. Atrial fibrillation (A(fib)), atrial flutter (A(fl)), and ventricular fibrillation (V-fib) are heart arrhythmias affecting predominantly senior citizens. An electrocardiogram (ECG) is a device serving to measure the ECG signal and diagnosis of an abnormal pattern which represents a heartbeat defects. Though it is possible to analyze these signals manually, in some cases it is a difficult task due to the often signal distortion by noise. Furthermore, manual analyzation of patterns is subjective and can lead to an inaccurate diagnosis. An automated computer-aided diagnosis (CAD) is a technique to eliminate these shortcomings. In this work, we present an 6-layer deep convolutional neural network (CNN) for automatic ECG pattern classification of the normal (N-r), A(fib), A(fl), and V-fib classes. This proposed CNN model requires simple feature extraction and no pre-processing of ECG signals. For two seconds ECG segments, the model obtained the accuracy of 97.78%, specificity and sensitivity of 98.82% and 99.76% respectively. This proposed system can be used as an assistant automatic tool in a clinical environment as a decision support system.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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 periodika

Applied Intelligence

ISSN

0924-669X

e-ISSN

—

Svazek periodika

49

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

3383-3391

Kód UT WoS článku

000482434300014

EID výsledku v databázi Scopus

2-s2.0-85065165094