Performance Evaluation of Epileptic Seizure Prediction Using Time, Frequency, and Time-Frequency Domain Measures

The prediction of epileptic seizures is crucial to aid patients in gaining early warning and taking effective intervention. Several features have been explored to predict the onset via electroencephalography signals, which are typically non-stationary, dynamic, and varying from person-to-person. In the former literature, features applied in the classification have shared similar contributions to all patients. Therefore, in this paper, we analyze the impact of the specific combination of feature and channel from time, frequency, and time-frequency domains on prediction performance of disparate patients. Based on the minimal-redundancy-maximal-relevance criterion, the proposed framework uses a sequential forward selection approach to individually find the optimal features and channels. Trained models could discriminate the pre-ictal and inter-ictal electroencephalography with a sensitivity of 90.2% and a false prediction rate of 0.096/h. We also present the comparison between the classification accuracy obtained by the optimal features, several features summarized from optimal features, and the complete set of features from three domains. The results indicate that various patient interpretations have a certain specificity in the selection of feature-channel. Furthermore, the detailed list of optimal features and summarized features are proffered for reference to those who research the corresponding database.

Automated summary

This study examines the impact of specific feature and channel combinations on predicting epileptic seizures in different patients. By selecting optimal features and channels based on the minimal-redundancy-maximal-relevance criterion, the trained models are able to effectively differentiate between pre-ictal and inter-ictal EEG signals. The detailed list of optimal features and summarized features provided in the study can serve as a reference for future research on this topic.