Hybrid Supervised-Unsupervised Learning Pipeline for EEG Anomaly Detection Using Autoencoders and 1D CNN Models
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Abstract
Seizure detection from electroencephalogram (EEG) signals remains a difficult problem because of the wide variation in patient patterns and the limited amount of labelled data. In this work, we developed a hybrid learning setup that blends a supervised 1D Convolutional Neural Network (CNN) with an unsupervised Autoencoder (AE). The CNN learns to recognise seizure-related patterns from labelled EEG segments, while the AE models typical EEG activity and signals abnormal deviations. We combined their predictions using three ensemble techniques-Soft Weighted Average, Soft Average, and Majority Voting-to stabilize performance and reduce false alarms. Tests on the Turkish Epilepsy EEG Dataset showed that this hybrid approach performed more reliably across patients than either model alone.
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