Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG

Epilepsy & Behavior : E&B
Aiyu YanJiwen Wang

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.

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Citations

Oct 7, 2015·Epilepsy & Behavior : E&B·Yudan XiaShujuan Geng
May 30, 2015·International Journal of Neural Systems·Cristian DonosAndreas Schulze-Bonhage
Oct 1, 2019·International Journal of Neural Systems·Guoyang LiuMinxing Geng
Apr 20, 2018·Frontiers in Physiology·Zhen GaoWanming Zhang
Mar 17, 2020·International Journal of Neural Systems·Yang LiBaosheng Li
May 27, 2020·Brain Informatics·Mohammad Khubeb SiddiquiNasir Hussain

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