Neural Network with L-M Algorithm for Arrhythmia Disease Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F22%3A00361192" target="_blank" >RIV/68407700:21220/22:00361192 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-89899-1_33" target="_blank" >https://doi.org/10.1007/978-3-030-89899-1_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-89899-1_33" target="_blank" >10.1007/978-3-030-89899-1_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Network with L-M Algorithm for Arrhythmia Disease Classification

Popis výsledku v původním jazyce

his paper presents a feedforward multilayer perceptron neural network with a Levenberg-Marquardt learning algorithm for recognizing arrhythmia disease from normal electrocardiogram (ECG) patterns. To the best of our knowledge, in the field of arrhythmia disease classification, classical approaches utilize either different QRS complex detection or feature reduction methods but not both at the same time; thus, this work provides an important contribution. A total of forty-four records were obtained from the MIT-BIH arrhythmia database to test the QRS complex detection method, and the obtained results were a specificity of 96.16% and a sensitivity of 98.03%. The best classification rate obtained using the presented approach was 98.27%.

Název v anglickém jazyce

Neural Network with L-M Algorithm for Arrhythmia Disease Classification

Popis výsledku anglicky

his paper presents a feedforward multilayer perceptron neural network with a Levenberg-Marquardt learning algorithm for recognizing arrhythmia disease from normal electrocardiogram (ECG) patterns. To the best of our knowledge, in the field of arrhythmia disease classification, classical approaches utilize either different QRS complex detection or feature reduction methods but not both at the same time; thus, this work provides an important contribution. A total of forty-four records were obtained from the MIT-BIH arrhythmia database to test the QRS complex detection method, and the obtained results were a specificity of 96.16% and a sensitivity of 98.03%. The best classification rate obtained using the presented approach was 98.27%.

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Networks and Systems

ISSN

2367-3370

e-ISSN

—

Svazek periodika

343

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

309-318

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85118191234