Epileptic Seizure Detection by Cascading Isolation Forest-based Anomaly Screening and EasyEnsemble

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; p. 1
• Main Authors: Guo, Yao, Jiang, Xinyu, Tao, Linkai, Meng, Long, Dai, Chenyun, Long, Xi, Wan, Feng, Zhang, Yuan, Van Dijk, Johannes, Aarts, Ronald M., Chen, Wei, Chen, Chen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: aEEG; Algorithms; Anomalies; Anomaly detection; Band-pass filters; Child; Convulsions & seizures; EEG; Electroencephalography; Epilepsy; Epilepsy - diagnosis; Feature extraction; Forestry; Forests; Humans; Hybrid systems; Labeling; Labelling; Learning; Low-pass filters; Machine learning; Modules; Screening; Segmentation; Segments; Seizure detection system; Seizures; Seizures - diagnosis; Sensitivity; Signal Processing, Computer-Assisted; supervised learning; unsupervised learning; Workload; Workloads

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amou...