An approach to seizure detection using an artificial neural network (ANN).

We have developed an EEG seizure detector based on an artificial neural network. The input layer of the ANN has 31 nodes quantifying the amplitude, slope, curvature, rhythmicity, and frequency components of EEG in a 2 sec epoch. The hidden layer has 30 nodes and the output layer has 8 nodes representing various patterns of EEG activity (e.g. seizure, muscle, noise, normal). The value of the output node representing seizure activity is averaged over 3 consecutive epochs and a seizure is declared when that average exceeds 0.65. Among 78 randomly selected files from 50 patients not in the original training set, the detector declared at least one seizure in 76% of 34 files containing seizures. It declared no seizures in 93% of 44 files not containing seizures. Four false detections during 4.1 h of recording yielded a false detection rate of 1.0/h. The detector can continuously process 40 channels of EEG with a 33 MHz 486 CPU. Although this method is still in its early stages of development, our results represent proof of the principle that ANN could be utilized to provide a practical approach for automatic, on-line, seizure detection.