EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes

Objectives: To test if a method for real-time detection of epileptic seizures based on electroencephalographic (EEG) analysis with simulated neuronal cell models can be modified to identify pre-seizure changes. Methods: Our EEG analysis method consists of two simulated leaky integrate and fire units (LIFU) connected to a signal preprocessing stage that marks parts of the EEG signals with slopes larger than a preset threshold Hth with unit pulses. The LIFUs change their spiking frequency depending on the rate and the synchrony of the impinging pulse trains. Here, we use our method in a high-sensitivity mode by setting Hth to low values, which causes the LIFUs to continuously spike during the interictal state. We test if the LIFUs spiking rates change before seizure onset. Results: We used 9 long-term EEGs (16±7 h) of 7 patients with drug resistant epilepsy. Fifteen seizures were analyzed and all were preceded by an increase of the time-averaged spiking rates SRav of the LIFUs. We defined a function FSz, which quantifies the changes of SRav. FSz increased and stayed above an individually set and fixed threshold 83±91 min (range: 4–330 min) before EEG seizure onset. Only two false alarms occurred. Conclusions: We conclude that EEG analysis with simulated neuronal cell models may be used to detect pre-seizure changes with high sensitivity and specificity.