Forecasting epilepsy from the heart rate signal.
Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30 s prior to seizure onset Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.
Kerem
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Recanati Institute for Maritime Studies, University of Haifa, Haifa, Israel. dankerem@research.haifa.ac.il
Geva
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D000465:Algorithms; D000818:Animals; D004569:Electroencephalography / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis / Q000503:physiopathology; D017143:Fuzzy Logic; D006339:Heart Rate; D006801:Humans; D051381:Rats; D012815:Signal Processing, Computer-Assisted