Computerized seizure detection of complex partial seizures

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.

Improvement in seizure detection performance by automatic adaptation to the EEG of each patient

An important problem in the use of automatic seizure detection during long-term epilepsy monitoring is that false detections can be very frequent, often because a paroxysmal but non-epileptiform pattern occurs repeatedly in a particular patient. We therefore introduce a method to reduce such patient-specific false seizure detections. The program "learns" about the false detections occurring in the first day of a prolonged monitoring session and attempts to eliminate similar patterns occurring during the remainder of the session. This method was evaluated in 20 patients having particularly high false detection rates. Seventy EEG sessions from 10 patients with scalp electrodes and 64 sessions from 10 patients with depth electrodes, covering a total of 2600 h were used in the evaluation. False detections were reduced by 61% (50% in scalp recordings and 71% in depth recordings), with only a 5% probability of losing true seizures. The average false detection rate in these patients fell from 3.25/h to 1.26/h. This significant reduction in false detections could also lead to lower detection thresholds and consequently to the detection of more true seizures.