Title :
Line length: an efficient feature for seizure onset detection
Author :
Esteller, R. ; Echauz, J. ; Tcheng, T. ; Litt, B. ; Pless, B.
Abstract :
A signal feature with low computational burden is presented as an efficient tool for seizure onset detection. The feature was evaluated over a total of. 1,215 hours of intracranial EEG signal from 10 patients. Results confirmed this feature as being useful for seizure onset detection yielding an average delay of 4.1 seconds, 0.051 false positives per hour, and one false negative on a subclinical seizure out of 111 seizures analyzed of which 23 were subclinical.
Keywords :
diseases; electroencephalography; feature extraction; fractals; medical signal processing; signal classification; epileptic seizure onset detection; fractal dimension; intracranial EEG; line length; low computational burden; normalization; patient-specific tunable parameter; preictal period; signal feature; summation indexes; Computer vision; Data mining; Delay; Electroencephalography; Feature extraction; Finite impulse response filter; Fractals; Frequency domain analysis; Intelligent systems; Nervous system;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1020545