MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021633" target="_blank" >RIV/62690094:18450/24:50021633 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/24:10257245

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0950705124009560?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0950705124009560?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.knosys.2024.112322" target="_blank" >10.1016/j.knosys.2024.112322</a>

Result language

angličtina

Original language name

MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals

Original language description

Approximately 65 million individuals experience epilepsy globally. Surgery or medication cannot cure more than 30% of epilepsy patients.However, through therapeutic intervention, anticipating a seizure can help us avoid it. According to previous studies, aberrant activity inside the brain begins a few minutes before the onset of a seizure, known as a pre-ictal state. Many researchers have attempted to anticipate the pre-ictal condition of a seizure; however, achieving high sensitivity and specificity remains challenging. Therefore, deep learning-based early diagnostic tools for epilepsy therapies using electroencephalogram (EEG) signals are urgently needed. Traditional methods perform well in binary epilepsy scenarios, such as normal vs. ictal, but poorly in ternary situations, such as ictal vs. normal vs. inter-ictal. This study proposes a multi-scale dilated convolution-based network (MD-DCNN) to predict seizures or epilepsy. Traditional DCNNs for epilepsy classification overfit due to insufficient training data (fewer subjects). Windowing 2-sec EEG recordings and extracting the frequency sub-band from each window prevents overfitting in deep networks, which lack training data. We convert each segmented window and its sub-bands into scalogram images and input them into MD-DCNN. The proposed MD-DCNN combines data from several scales without narrowing the acquisition domain. Integrating detailed information into high-level semantic features improves network interpretation and classification. The proposed MD-DCNN is evaluated for two-class, three-class, and cross-database strategy problems using three publicly accessible databases. Experiments show that the MD-DCNN statistically performs better than 13 other current approaches. This demonstrates its potential for developing equipment capable of measuring, monitoring, and recording EEG signals to diagnose epilepsy. © 2024 Elsevier B.V.

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

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Knowledge-based systems

ISSN

0950-7051

e-ISSN

1872-7409

Volume of the periodical

301

Issue of the periodical within the volume

October

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

"Article number: 112322"

UT code for WoS article

001291284100001

EID of the result in the Scopus database

2-s2.0-85200588436