Machine Learning Models for Probability Classification in Spectrographic EEG Seizures Dataset

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.