Adaptive detection method based on instantaneous bandwidth feature for seizure onset

Seizure is one of the most common neurological system diseases. Seizure onset is usually identified by the starting point of seizure onset in the EEG measurement record, which can help doctor to conduct the seizure diagnosis and the alarm of the patient state. In order to improve the performance of seizure onset detection algorithm, this paper proposes an adaptive bandwidth feature based on EEG signal instantaneous parameters, which can be used to improve seizure onset detection accuracy. Firstly, the intrinsic mode functions (IMFs) of the EEG signal is calculated using empirical mode decomposition (EMD). Then, the Hilbert transform is conducted on a specific order IMF to get the analytical signal. The analytical signal is used to calculate the instantaneous amplitude and frequency. The weights are introduced in the bandwidth features of the EEG signal, and the adaptive bandwidth features used for seizure detection are obtained. Finally, the seizure onset detection is achieved using these adaptive bandwidth features. In order to verify the proposed method, the clinic EEG data of the epilepsy patients with a length of 118 hours and 49 minutes were used to conduct experiment, the experiment results show that the sensitivity, specificity and accuracy of the adaptive bandwidth features are improved obviously compared with the original features; the adaptive bandwidth features improve the seizure onset detection accuracy and reduce the delay time, so that timely treatment can be taken, and the proposed method provides an significant basis for seizure clinic detection.