title: An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms.
abstract: Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an EEG that characterize seizure activity. Conventional feature extraction methods using time-series analysis, spectral analysis and nonlinear dynamic analysis have advanced in recent years to improve detection. The computational complexity has also increased to obtain a higher detection rate. This study aimed to develop an efficient feature extraction method based on Hjorth's mobility to reduce computational complexity while maintaining high detection accuracy. A new feature extraction method was proposed by computing the spectral power of Hjorth's mobility components, which were effectively estimated by differentiating EEG signals in real-time. Using EEG data in five epileptic patients, this method resulted in a detection rate of 99.46% between interictal and epileptic EEG signals and 99.78% between normal and epileptic EEG signals, which is comparable to most advanced nonlinear methods. These results suggest that the spectral features of Hjorth's mobility components in EEG signals can represent seizure activity and may pave the way for developing a fast and reliable epileptic seizure detection method.
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
authors.forename: Jae-Hwan
authors.initials: JH
authors.lastname: Kang
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
authors.forename: Yoon Gi
authors.initials: YG
authors.lastname: Chung
authors.affiliation: Department of Human and Systems Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea. Electronic address: spkim@unist.ac.kr.
authors.forename: Sung-Phil
authors.initials: SP
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D016208:Databases, Factual; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005583:Fourier Analysis; D006801:Humans; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
keywords: Autoregressive model; Electroencephalography; Epileptic seizure detection; Hjorth׳s mobility; Time-domain differentiation