title: Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG.
abstract: Automatic detection of seizures has vital significance for epileptic diagnosis and can efficiently reduce the workload of the medical staff. In this study, a novel seizure detection method based on Stockwell transform is proposed for intracranial long-term EEG data. The Stockwell transform is employed to obtain the time-frequency representation of the EEG signals, and then the power spectral density is calculated in the time-frequency plane to characterize the behavior of EEG recordings. After that, a classifier based on gradient boosting algorithm is used to make the classification. Finally, the postprocessing is utilized on the outputs of the classifier to obtain more stable and accurate detection results, which includes Kalman filter, threshold judgment, and collar technique. The performance of this method is assessed on the publicly available EEG database which contains approximately 533h of intracranial EEG recordings. The experimental results indicate that the proposed method can achieve a satisfactory sensitivity of 94.26%, a specificity of 96.34%, as well as a very short delay time of 0.56s.
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute of Shandong University, Suzhou 215123, China.
authors.forename: Aiyu
authors.initials: A
authors.lastname: Yan
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute of Shandong University, Suzhou 215123, 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, Jinan 250100, China; Suzhou Institute of Shandong University, Suzhou 215123, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Yuan
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute of Shandong University, Suzhou 215123, China.
authors.forename: Shasha
authors.initials: S
authors.lastname: Yuan
authors.affiliation: Qilu Hospital, Shandong University, Jinan 250100, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Qilu Hospital, Shandong University, Jinan 250100, China.
authors.forename: Xiuhe
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Qilu Hospital, Shandong University, Jinan 250100, China.
authors.forename: Jiwen
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Boosting algorithm; Electroencephalogram (EEG); Power spectral density; Seizure detection; Stockwell transform