标题：Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG
作者：Zhou, Weidong; Liu, Yinxia; Yuan, Qi; Li, Xueli
作者机构：[Zhou, Weidong; Liu, Yinxia; Yuan, Qi; Li, Xueli] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.
通讯作者地址：[Zhou, WD]Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.
来源：IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
关键词：Bayesian linear discriminant analysis (BLDA); lacunarity; seizure; detection; wavelet transform
摘要：Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.