Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine

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...

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Vydáno v:	Scientific reports Ročník 14; číslo 1; s. 10667 - 21
Hlavní autoři:	Saeedinia, Samaneh Alsadat, Jahed-Motlagh, Mohammad Reza, Tafakhori, Abbas, Kasabov, Nikola Kirilov
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 09.05.2024 Nature Publishing Group Nature Portfolio
Témata:	631/114 631/114/116 631/114/1305 631/114/1386 631/114/2408 692/53 Adult Algorithms Biomarkers Biomarkers - analysis Brain - physiopathology Brain research Classification Deep BiLSTM Deep Learning EEG EEG classification Electroencephalography - methods Epilepsy Epilepsy - diagnosis Epilepsy - physiopathology Female Firing pattern Headache Humanities and Social Sciences Humans Male Migraine Migraine Disorders - diagnosis Migraine Disorders - physiopathology multidisciplinary Neural networks Neural Networks, Computer Pattern recognition Pilot Projects Reservoirs Science Science (multidisciplinary) Spike encoding algorithm NeuCube Deep BiLSTM Epilepsy Migraine Deep reservoir SNN EEG classification Pattern recognition Spike encoding algorithm
ISSN:	2045-2322, 2045-2322
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-60996-6