Forecasting epilepsy from the heart rate signal

Dan H. Kerem, A. B. Geva

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish
Pages (from-to)230-239
Number of pages10
JournalMedical and Biological Engineering and Computing
Issue number2
StatePublished - 1 Mar 2005


  • ECG
  • Forecasting
  • Heart rate variability
  • Ictal tachycardia
  • Seizure
  • Unsupervised fuzzy clustering

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications


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