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+/-7h) of 7 patients with drug resistant epilepsy. Fifteen seizures were analyzed and all were preceded by an increase of the time-averaged spiking rates SR a v of the LIFUs. We defined a function F S z , which quantifies the changes of SR a v . F S z increased and stayed above an individually set and fixed threshold 83+/-91min (range: 4-330min) 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.