Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data

We have developed and tested "off-line" an artificial neural network (ANN) that successfully detects epileptiform discharges (EDs) when trained on EEG records marked by an electroencephalographer (EEGer). The system was trained on both parameterized and raw EEG data and can process 49 channels of EEG data in real time on an 80486/33 MHz personal computer, making it capable of processing EEG on-line in long-term monitoring units. Our detector consists of 2 stages: (1) a threshold detector identifies candidate EDs in 4-channel bipolar chains within the recording montage, parameterizes them and then passes these data to the second stage; (2) a 3-layer feed-forward ANN decides if a candidate wave form is an ED. The intersection of detector sensitivity and selectivity curves, or crossover threshold, for 10 patients from our Epilepsy Monitoring Unit occurred at 73% for parameterized EEG data and at 46% for "raw" EEG data. The ANN could be adapted to different EEGers' styles by changing the ANN output threshold for accepting candidate wave forms as EDs. In this "proof of principle" study the detector was trained on EEGs from 10 Johns Hopkins Hospital Epilepsy Monitoring Unit (JHH EMU) patients. We used different EEGs from the same patients for testing. Current testing should demonstrate that the ANN detector can generalize to previously "unseen" patients. This study shows that ANNs offer a practical solution for automated, real time ED detection that uses, standard, inexpensive computers, is easily adjustable to individual EEGer style and can produce sensitivities and selectivities similar to those of EEGers.