EEG Signal Classification Using Fast Fourier Transform and Convolutional Neural Networks: A Hybrid Deep Learning Approach for Binary Classification of Normal and Abnormal Brain Activity
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This study presents a prototype that classifies full EEG recordings as either seizure-labelled or non-seizure-labelled. The method first uses Fast Fourier Transform (FFT) to convert each EEG into the frequency domain, then applies a two-dimensional Convolutional Neural Network (2D CNN) for classification. It was observed that many trainees and students found EEG interpretation difficult. Labelling was straightforward: any recording with at least one labelled seizure was marked as seizure labelled, and the rest as non-seizure-labelled. Training used two public datasets from PhysioNet: the pediatric CHB-MIT and adult Siena scalp EEG databases, with seizure labels from patient annotation files. To give the prototype a realistic run, eleven anonymised EEG recordings from Aster Royal Al Raffah Hospital in Muscat, Oman, were taken and used for external validation; out of those eleven, five were labelled as non-seizure recordings and six as seizure recordings. In early tests, the prototype achieved about 88% accuracy, with F1-scores of 0.95 and 0.91 for the two classes, and 82% accuracy on the hospital data. These results are preliminary, and full validation is planned. The tool is designed to help trainees during supervision, not to diagnose patients or identify seizures within recordings.
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