A Lightweight Autoencoder Model for Epileptic Seizure Detection

Epilepsy is a neurological disorder marked by recurrent and unpredictable disturbances in brain activity, leading to epileptic seizures (ES). One approach of diagnosing these seizures involves analyzing ElectroEncephaloGraphic (EEG) signals, which are highly dynamic and complex. Recently, researchers have been implementing Machine Learning (ML) Deep Learning (DL) techniques to perform automatic seizure detection of these EEG signals. ML models require extensive feature extraction techniques compared with DL ones. Despite the promising results of DL models in the detection task, their computational cost is still a challenge. Moreover, DL models require a large amount of training data which is not always available. Hence, in this paper, a hybrid channel-wise lightweight automatic seizure detection model based on vanilla autoencoders and K-NN classifier is introduced. The model is able to generate almost real-like data that can be used as data augmentation technique and increasing the dataset spread for DL models. Mean Square Error (MSE), Pearson Correlation Coefficient (PCC), and Power Spectral Density (PSD) similarity were used to evaluate the quality of the generated signal. Compared to other methods, this model provided a competitive average reconstruction loss of 0.4515, an average precision of 93.69%, and a low parameter count of 41.152k. The lightweight nature of this model could potentially be used in wearable clinical devices for real-time seizure detection.