Patient-Independent Epileptic Seizure Detection with Reduced EEG Channels and Deep Recurrent Neural Networks

Epileptic seizures affect around 1% of people worldwide and have an enormous impact on the quality of life as well as the health of each patient. Electroencephalography (EEG) is widely used to diagnose epilepsy and detect seizures. Automatic detection and documentation of epileptic seizures using EEG signals would help neurologists evaluate the course of disease of each patient individually. As scalp EEG systems are not suited to be worn in everyday life situations, there is a need for mobile EEG systems to permanently record EEG signals. An approach for such mobile devices consists of using behind-the-ear (BTE) electrodes, leading to a reduction in electrode channels. To address this reduction, we investigated the influence of different scalp EEG channel arrangements on the detection of epileptic seizures. Raw EEG signals have been used as input for a long short-term memory (LSTM) recurrent neural network (RNN), as well as a combination of a convolutional neural network (CNN) and LSTM to classify ictal and inter-ictal phases. When using all channels of the 10-20 EEG cap system, the CNN-LSTM model achieved a sensitivity of 73%, with fewer than two seizures being falsely detected per hour. The usage of BTE channels as input to the proposed epileptic seizure detection produced a promising sensitivity of 68% with around 10 false alarms per hour.