An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods
Article
Article Title | An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods |
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ERA Journal ID | 3391 |
Article Category | Article |
Authors | Shen, Mingkan (Author), Wen, Peng (Author), Song, Bo (Author) and Li, Yan (Author) |
Journal Title | Biomedical Signal Processing and Control |
Journal Citation | 77, pp. 1-8 |
Article Number | 103820 |
Number of Pages | 8 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1746-8094 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bspc.2022.103820 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1746809422003421 |
Abstract | Epilepsy is one of the most common complex brain disorders which is a chronic non-communicable disease caused by paroxysmal abnormal super-synchronous electrical activity of brain neurons. This paper proposed an electroencephalogram (EEG) based real-time approach to detect epilepsy seizures. Discrete wavelet transform and eight eigenvalues’ algorithms are applied to extract features in different sub-frequency bands. Then support vector machine is employed for three-classes classification of health control, seizure free and seizure active, and finally RUSBoosted tree Ensemble method is used for real-time seizure onset detection. The proposed algorithm is evaluated using two public datasets: one short-term dataset named UB and one long-term dataset named CHB-MIT. The results show that the algorithm achieves 97% accuracy and 96.67% sensitivity in the three-classes classification of health control, seizure-free and seizure-active groups in UB dataset, and 96.38% accuracy, 96.15% sensitivity, 3.24% false positive rate for the real time seizure onset detection in CHB-MIT Dataset. |
Keywords | Discrete wavelet transform; EEG; Real-time seizure detection; RUSBoosted tree Ensemble; Support vector machine |
Related Output | |
Is part of | Real-time epilepsy seizure detection and brain connectivity analysis using electroencephalogram |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
400303. Biomechanical engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
This article is part of a UniSQ Thesis by publication. See Related Output. | |
Byline Affiliations | School of Mechanical and Electrical Engineering |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q78y4/an-eeg-based-real-time-epilepsy-seizure-detection-approach-using-discrete-wavelet-transform-and-machine-learning-methods
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