Convolutional Neural Networks for Seizure Detection: A Study on Training Strategies.

Area of Science:

• * Computational Neuroscience
• * Medical Image Analysis
• * Machine Learning

Background:

• * Convolutional Neural Networks (CNNs) are increasingly used for analyzing electroencephalogram (EEG) data.
• * Accurate seizure detection from EEG is crucial for patient diagnosis and treatment.
• * Existing CNN models for seizure classification can benefit from optimized training strategies.

Purpose of the Study:

• * To evaluate the efficacy of image processing-derived training strategies for EEG seizure classification.
• * To enhance the performance of a baseline CNN classifier for seizure detection.
• * To identify the most effective combination of training techniques for improving classification metrics.

Main Methods:

• * Applied image processing training strategies including random cropping, dropout, mixup, and ensembling to a CNN model.
• * Trained and evaluated the CNN on EEG recordings for seizure classification.
• * Compared the performance of individual strategies and their combinations against a baseline classifier.

Main Results:

• * Random cropping, dropout, mixup, and ensembling individually improved CNN performance.
• * A combination of random cropping, mixup, and ensembling yielded the best results.
• * The optimized model achieved an improved Area Under the Curve (AUC) from 0.957 to 0.981 and F1-score from 71.0% to 77.9%.

Conclusions:

• * Training strategies from image processing can significantly enhance CNN-based seizure detection from EEG.
• * Optimizing CNN training is critical for improving the accuracy of seizure classification.
• * The study highlights the potential of advanced training techniques for clinical applications in epilepsy monitoring.