Author(s): Khan YU, Gotman J
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Abstract BACKGROUND: Automatic seizure detection is often used during long-term monitoring, and is particularly important during intracerebral investigations. Existing methods make many false detections, particularly in intracerebral electroencephalogram (EEG) because of frequent large amplitude rhythmic activity bursts that are non-epileptiform. OBJECTIVE: To develop a seizure detection method for intracerebral monitoring that is as sensitive as existing methods but has fewer false detections. METHODS: To capture the rhythmic nature of seizure discharges, we developed a wavelet-based method, examining how different frequency ranges fluctuate compared to the background. In particular, the system remembers rhythmic bursts occurring commonly in the background to avoid detecting them as seizures. RESULTS: The method was evaluated on test data from 11 patients, including 229 h and 66 seizures, and its performance compared to the method of Gotman (Electroencephalogr clin Neurophysiol 76 (1990) 317). Detection sensitivity was unchanged at close to 90\%, but false detections were reduced from 2.4 to 0.3/h. CONCLUSIONS: Perfect sensitivity is unlikely because the morphology of seizure discharges is so variable. Nevertheless, the 87\% sensitivity obtained in the combined training and testing data is quite high. We reduced the average false alarm rate to one per 3 h of recording, or 6 per 24-h period. Given how rapidly one can decide visually that a detection is erroneous, false detections should not cause any burden to the reviewer. SIGNIFICANCE: In intracerebral EEG it is possible to detect seizures automatically with high sensitivity and high specificity.
This article was published in Clin Neurophysiol
and referenced in Journal of Computer Science & Systems Biology