title: Automatic seizure detection using diffusion distance and BLDA in intracranial EEG.
abstract: Approximately 1% of the world's population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.
authors.affiliation: School of Information Science and Engineering, Shandong University, PR China.
authors.forename: Shasha
authors.initials: S
authors.lastname: Yuan
authors.affiliation: School of Information Science and Engineering, Shandong University, PR China. Electronic address: wdzhou@sdu.edu.cn.
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.affiliation: School of Information Science and Engineering, Shandong University, PR China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Yuan
authors.affiliation: School of Information Science and Engineering, Shandong University, PR China.
authors.forename: Yanli
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Information Science and Engineering, Shandong University, PR China.
authors.forename: Qingfang
authors.initials: Q
authors.lastname: Meng
mesh_terms: D001499:Bayes Theorem; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography; D001330:Electronic Data Processing / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D012680:Sensitivity and Specificity; D013997:Time Factors
keywords: Bayesian linear discriminant analysis (BLDA); Diffusion distance; Discrete wavelet transform (DWT); EEG; Seizure detection