Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function

Goal: The accurate automatic detection of epileptic seizures is very important in long-term electroencephalogram (EEG) recordings. In this study, the wavelet decomposition and the directed transfer function (DTF) algorithm were combined to present a novel wavelet-based directed transfer function (WDTF) method for the patient-specific seizure detection. Methods: First, five subbands were extracted from 19-channel EEG signals by using wavelet decomposition in a sliding window. Second, the information flow characteristics of five subbands and full frequency band of EEG signals were calculated by the DTF method. The intensity of the outflow information was then used to reduce the feature dimensionality. Finally, all features were combined to identify interictal and ictal EEG segments by the support vector machine classifier. Results: By using fivefold cross validation, the proposed method had achieved excellent performance with the average accuracy of 99.4%, the average selectivity of 91.1%, the average sensitivity of 92.1%, the average specificity of 99.5%, and the average detection rate of 95.8%. Conclusion: The WDTF method is able to enhance seizure detection results in long-term EEG recordings of focal epilepsy patients. Significance: This study may lead to the development of seizure detection system with high performance, thus reducing the workload of epileptologists and facilitating to take corresponding steps promptly after the seizure onset. The high-frequency activity in the epilepsy brain may be of great importance for investigating the pathological mechanism and treatment of seizure.