A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures
Antonio Quintero-Rincón1
Carlos D'Giano2
Hadj Batatia3
1.Epilepsy and Telemetry Integral Center, Foundation for the Fight against Pediatric Neurological Disease, Monta(n)eses 2325, Buenos Aires C1428AQK, Argentina;Computer Science Research Institute of Toulouse-National Polytechnic Institute of Toulouse, University of Toulouse, Toulouse, Cedex 7 B.P.7122-31071, France2.Epilepsy and Telemetry Integral Center, Foundation for the Fight against Pediatric Neurological Disease, Monta(n)eses 2325, Buenos Aires C1428AQK, Argentina3.Computer Science Research Institute of Toulouse-National Polytechnic Institute of Toulouse, University of Toulouse, Toulouse, Cedex 7 B.P.7122-31071, France
摘要：The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals.The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals.The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique.The model fitting was assessed using four statistical parameters,which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events.The proposed method was applied to 66 epochs from the Children Hospital Boston database.Results showed that the method achieved fast and accurate detection of epileptic seizures,with a 92％ sensitivity,96％ specificity,and 94.1％ accuracy.
机标关键词：
分类号：R742.1(神经病学与精神病学)
论文发表日期：2020-05-30
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：8( 205-212 )
英文信息