title: Model-based seizure detection method using statistically optimal filters.
abstract: In this paper, a model-based seizure detection method using statistically optimal filters (SONFs) is presented. A template seizure from a patient is first selected and the basis functions required by the SONF are derived from this template seizure using wavelet transform. Subsequent EEG (electroencephalogram) recording is processed by the SONF and the output represents the noise-free estimate of the seizure. The energy ratio between the output and the input of the SONF is calculated and used as the test statistic for the seizure detection. Experiments using the SEEG (stereoelectroencephalogram, or intracerebral EEG) recordings of two patients show that this is an effective and promising method, with the possibility of reduced false detections.
authors.affiliation: Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada. l_shi@ece.concordia.ca
authors.forename: Liying
authors.initials: L
authors.lastname: Shi
authors.forename: Rajeev
authors.initials: R
authors.lastname: Agarwal
authors.forename: M N S
authors.initials: MN
authors.lastname: Swamy