Automatic seizure detection by convolutional neural networks with computational complexity analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50019875" target="_blank" >RIV/62690094:18450/23:50019875 - isvavai.cz</a>

Alternative codes found

RIV/62690094:18470/23:50019875

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0169260722006587?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0169260722006587?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cmpb.2022.107277" target="_blank" >10.1016/j.cmpb.2022.107277</a>

Result language

angličtina

Original language name

Automatic seizure detection by convolutional neural networks with computational complexity analysis

Original language description

Background and Objectives: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagno-sis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems.Methods: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network.Results: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset.Conclusions: Through the approach to detection, the system offers an optimized solution for seizure di-agnosis health problems. The proposed solution should be implemented in all clinical or home environ-ments for decision support.(c) 2022 Elsevier B.V. All rights reserved.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

2023

Confidentiality

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

Name of the periodical

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

1872-7565

Volume of the periodical

229

Issue of the periodical within the volume

February

Country of publishing house

IE - IRELAND

Number of pages

8

Pages from-to

"Article Number: 107277"

UT code for WoS article

000897677600004

EID of the result in the Scopus database

2-s2.0-85143536688