A unified multi-level spectral–temporal feature learning framework for patient-specific seizure onset detection in EEG signals

作者：

Highlights：

• A novel multi-level spectral–temporal feature learning framework constructed for EEG seizure onset detection.

• Auxiliary supplementary spectral–temporal information attained.

• A public dataset with multiple epileptic patients employed to test robustness.

• High classification performance obtained.

摘要

•A novel multi-level spectral–temporal feature learning framework constructed for EEG seizure onset detection.•Auxiliary supplementary spectral–temporal information attained.•A public dataset with multiple epileptic patients employed to test robustness.•High classification performance obtained.

论文关键词：Electroencephalography (EEG),Seizure onset detection,Multi-domain feature extraction,Deep learning,Multivariate multiscale sample entropy,Common spatial pattern

论文评审过程：Received 13 November 2019, Revised 10 June 2020, Accepted 15 June 2020, Available online 15 July 2020, Version of Record 18 July 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106152