A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

Article


Tawhid, Md. Nurul Ahad, Siuly, Siuly and Li, Tianning. 2022. "A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG." IEEE Transactions on Instrumentation and Measurement. 71, pp. 1-11. https://doi.org/10.1109/TIM.2022.3217515
Article Title

A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

ERA Journal ID979
Article CategoryArticle
AuthorsTawhid, Md. Nurul Ahad (Author), Siuly, Siuly (Author) and Li, Tianning (Author)
Journal TitleIEEE Transactions on Instrumentation and Measurement
Journal Citation71, pp. 1-11
Article Number4010211
Number of Pages11
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN0018-9456
1557-9662
Digital Object Identifier (DOI)https://doi.org/10.1109/TIM.2022.3217515
Web Address (URL)https://ieeexplore.ieee.org/document/9931160
Abstract

Epilepsy (EP) is a severe neurological disorder characterized by recurrent seizures, which increases the risk of death three times more than normal. Currently, electroencephalography (EEG) has emerged as a highly promising technique for the diagnosis of EP. The majority of current EEG-based EP detection research has employed a variety of deep-learning (DL)-based models, but most of the approaches suffer from poor generalizability, optimal design, and performance rates. To address these issues, this study aims to develop an efficient framework based on the deep spatiotemporal neural network called convolutional long short-term memory (ConvLSTM) for EP detection from EEG signals. In the proposed model, first standard 19-channel EEG data are selected and resampled at 256 Hz and then those signals are segmented into 3-s time frames. Afterward, the segmented data are fed as input to the ConvLSTM model for identifying epileptic patients from normal subjects. To generalize the proposed model, we have tested it on two different datasets with varying population sizes. We have used the five-fold cross-validation and leave-one-out cross-validation (LOOCV) schemes to eliminate the experiment’s biases. To further validate the proposed framework, we have carried out various ablation studies. The experimental results demonstrate that the proposed model outperforms the current state-of-the-art results for the studied datasets, making it suitable for use as an automated system for the diagnosis of EP.

KeywordsConvolutional long short-term memory (ConvLSTM), deep learning (DL), electroencephalography (EEG), epilepsy (EP)
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460299. Artificial intelligence not elsewhere classified
400399. Biomedical engineering not elsewhere classified
400607. Signal processing
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsVictoria University
School of Mathematics, Physics and Computing
Institution of OriginUniversity of Southern Queensland
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