Neural Network with L-M Algorithm for Arrhythmia Disease Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F22%3A43896306" target="_blank" >RIV/44555601:13440/22:43896306 - isvavai.cz</a>
Result on the web
<a href="http://10.1007/978-3-030-89899-1_33" target="_blank" >http://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>
Alternative languages
Result language
angličtina
Original language name
Neural Network with L-M Algorithm for Arrhythmia Disease Classification
Original language description
This 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 ?eld of arrhythmia disease classi?cation, 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 speci?city of 96.16% and a sensitivity of 98.03%. The best classi?cation rate obtained using the presented approach was 98.27%.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances on P2P, Parallel, Grid, Cloud and Internet Computing
ISBN
978-3-030-89899-1
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
20
Pages from-to
309-318
Publisher name
Springer Nature
Place of publication
Basel
Event location
Fukuoka, Japan
Event date
Oct 28, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000722277600033