Model-based seizure detection method using statistically optimal filters

Liying Shi; Rajeev Agarwal; M N S Swamy

doi:10.1109/IEMBS.2004.1403086

Model-based seizure detection method using statistically optimal filters

Conf Proc IEEE Eng Med Biol Soc. 2004:2006:45-8. doi: 10.1109/IEMBS.2004.1403086.

Authors

Liying Shi¹, Rajeev Agarwal, M N S Swamy

Affiliation

¹ Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada. l_shi@ece.concordia.ca

PMID: 17271599
DOI: 10.1109/IEMBS.2004.1403086

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.