WSEAS Transactions on Biology and Biomedicine
Print ISSN: 1109-9518, E-ISSN: 2224-2902
Volume 21, 2024
Machine Learning Models for Probability Classification in Spectrographic EEG Seizures Dataset
Authors: , ,
Abstract: The examination of brain signals, namely the Electroencephalogram (EEG) signals, is an approach
to possibly detect seizures of the brain. Due to the nature of these signals, deep learning techniques have
offered the opportunity to perform automatic or semi-automatic analysis which could support decision and
therapeutical approaches. This paper focuses on the possibility of classifying EEG seizure using convolutional
layers (namely EfficientNetV2 architectures, i.e., EfficientNetV2S and EfficientNetV2B2), Long Short-Term
Memory (LSTM) units, and fine-tuned mechanisms of attention. We use these techniques to untangle the
complexity of these signals and accurately predict seizures. The proposed system provided interesting results
with an 86.45% accuracy under the Kullback-Leibler Divergence loss of 0.95. Moreover, these results showed
that embedding LSTM layers deeply increases the quality of the results since these layers support the analysis
of the spatial-temporal dynamics of the EEG signals. On the other hand, it is important to mention that
hardware limitations could affect these results and therefore it is important, when setting this architectural
system, to fine-tune the data set and balance the performance vs the computational cost of the process.
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Keywords: Machine Learning Models, Attention Mechanism, Probabilistic Classification, EEG, Support-Decision System, Kullback-Leibler Divergence
Pages: 260-271
DOI: 10.37394/23208.2024.21.27