Epileptic Seizure Prediction Based on Synchroextracting Transform and Sparse Representation

Feature extraction is crucial in machine learning and EEG analysis, where raw data often contains excess information. The prominence of machine learning has led to the development of numerous feature extraction methods over the past decade. This paper introduces an efficient feature extraction method that demonstrates superior experimental results. We employed the Synchroextracting Transform (SET) and Sparse Representation (SR) for enhanced feature extraction in epileptic EEG analysis. SET is a recently developed signal transformation technique, and SR effectively extracts information from multi-dimensional data. Our goal is to enhance time-frequency (TF) resolution using the SET-SR method, which offers a TF representation more concentrated with energy than traditional TF analysis methods. SR decomposes SET multi-dimensional sub-signals to accurately predict epileptic seizures. The significance of this feature extraction method was evaluated using a k-Nearest Neighbor (k-NN) algorithm, a traditional machine learning technique. Applying the SET-SR with the k-NN, we achieved an average accuracy of 99.48% on the CHB-MIT database and 100% accuracy on the Bonn University database in classifying pre-seizure signals. The SET-SR effectively detects pre-seizure signals, showing promise for developing an efficient patient-specific seizure prediction algorithm based on EEG data. Our findings demonstrate that enhanced feature extraction can reliably identify pre-seizure signals with high precision, even when using classical machine learning methods like k-NN. This research underscores the importance of feature extraction in EEG signal analysis and suggests that diverse classification methods can be employed for real-time seizure prediction while maintaining high accuracy.