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

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...

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Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 30; s. 1
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
ISSN:1534-4320, 1558-0210, 1558-0210
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Shrnutí: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 amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2022.3163503