Automatic seizure detection by convolutional neural networks with computational complexity analysis

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/62690094:18470/23:50019875

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic seizure detection by convolutional neural networks with computational complexity analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic seizure detection by convolutional neural networks with computational complexity analysis

Popis výsledku anglicky

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.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

1872-7565

Svazek periodika

229

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

8

Strana od-do

"Article Number: 107277"

Kód UT WoS článku

000897677600004

EID výsledku v databázi Scopus

2-s2.0-85143536688