Overcoming temporal and sequential data challenges in electroencephalography for harmful brain activity classificationOA

Background:Early detection of harmful brain activity in critically ill patients using electroencephalography(EEG)is vital for timely and effective clinical intervention.Automating EEG analysis with deep learning techniques holds significant promise for enhancing diagnostic efficiency and accuracy.Methods:We implemented EfficientNetB2,which leverages convolutional neural networks with a novel Temporal Squeeze-and-Excitation module to capture temporal EEG features,and WaveNet,a sequential model designed to effectively model temporal dependencies in EEG data using dilated causal convolutions and temporal self-attention.Both models were trained and evaluated using a publicly available EEG dataset,with performance assessed via 4-fold cross-validation and a step-wise learning rate reduction strategy.Results:Our results demonstrate a significant reduction in training loss from 0.6459 to 0.3055 and validation loss from 0.9602 to 0.5719 over six epochs.Consistent improvements were observed across cross-validation folds,highlighting the robustness of the models.Additionally,ensemble learning of the two architectures further enhanced classification performance.Conclusion:This comparative analysis sheds light on the strengths and limitations of EfficientNetB2 and WaveNet for automated harmful brain activity detection in EEG signals.The findings contribute to the advancement of reliable and efficient deep learning models,paving the way for their clinical application in managing critically ill patients.