A Aarabi1, R Fazel-Rezai, Y Aghakhani. 1. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. aarabi@EE.UManitoba.CA
Abstract
OBJECTIVE: We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy. METHODS: We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system. RESULTS: The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis. CONCLUSION: The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns. SIGNIFICANCE: This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.
OBJECTIVE: We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy. METHODS: We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system. RESULTS: The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis. CONCLUSION: The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns. SIGNIFICANCE: This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.
Authors: Alan Wl Chiu; Miron Derchansky; Marija Cotic; Peter L Carlen; Steuart O Turner; Berj L Bardakjian Journal: Biomed Eng Online Date: 2011-04-19 Impact factor: 2.819
Authors: Sabato Santaniello; Samuel P Burns; Alexandra J Golby; Jedediah M Singer; William S Anderson; Sridevi V Sarma Journal: Epilepsy Behav Date: 2011-12 Impact factor: 2.937