Neural Network with L-M Algorithm for Arrhythmia Disease Classification

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Neural Network with L-M Algorithm for Arrhythmia Disease Classification
Original language description
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%.
Czech name
—
Czech description
—

Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)