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Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.
Saeedinia, Samaneh Alsadat; Jahed-Motlagh, Mohammad Reza; Tafakhori, Abbas; Kasabov, Nikola Kirilov.
Afiliação
  • Saeedinia SA; Complex Systems Research Laboratory, Iran University of Science and Technology, Tehran, Iran.
  • Jahed-Motlagh MR; Complex Systems Research Laboratory, Iran University of Science and Technology, Tehran, Iran. Jahedmr@iust.ac.ir.
  • Tafakhori A; Department of Neurology, School of Medicine, Iranian Center of Neurological Research, Tehran University of Medical Sciences, Tehran, Iran.
  • Kasabov NK; School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand. nkasabov@aut.ac.nz.
Sci Rep ; 14(1): 10667, 2024 05 09.
Article em En | MEDLINE | ID: mdl-38724576
ABSTRACT
The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Biomarcadores / Redes Neurais de Computação / Eletroencefalografia / Epilepsia / Transtornos de Enxaqueca Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Biomarcadores / Redes Neurais de Computação / Eletroencefalografia / Epilepsia / Transtornos de Enxaqueca Idioma: En Ano de publicação: 2024 Tipo de documento: Article