Paper Info
Title
A Progressive Deep Wavelet Cascade Classification Model For Epilepsy Detection
Abstract
Automatic epileptic seizure detection according to EEG recordings is helpful for neurologists to identify an epilepsy occurrence in the initial anti-epileptic treatment. To quickly and accurately detect epilepsy, we proposed a progressive deep wavelet cascade classification model (PDWC) based on the discrete wavelet transform (DWT) and Random Forest (RF). Different from current deep networks, the PDWC mimics the progressive object identification process of human beings with recognition cycles. In every cycle, enhanced wavelet energy features at a specific scale were extracted by DWT and input into a set of cascade RF classifiers to realize one recognition. The recognition accuracy of PDWC is gradually improved by the fusion of classification results produced by multiple recognition cycles. Moreover, the cascade structure of PDWC can be automatically determined by the classification accuracy increment between layers. To verify the performance of the PDWC, we respectively applied five traditional schemes and four deep learning schemes to four public datasets. The results show that the PDWC is not only superior than five traditional schemes, including KNN, Bayes, DT, SVM, and RF, but also better than deep learning methods, i.e. convolutional neural network (CNN), Long Short-Term Memory (LSTM), multi Grained Cascade Forest (gcForest) and wavelet cascade model (WCM). The mean accuracy of PDWC for all subjects of all datasets reaches to 0.9914. With a flexible structure and less parameters, the PDWC is more suitable for the epilepsy detection of diverse EEG signals.
Year
DOI
Venue
2021
10.1016/j.artmed.2021.102117
ARTIFICIAL INTELLIGENCE IN MEDICINE
Keywords
DocType
Volume
EEG, Epilepsy detection, Discrete wavelet transform, Random forest, Cascade structure
Journal
118
ISSN
Citations 
PageRank 
0933-3657
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Hong He100.34
Xinyue Liu200.34
Yong Hao300.34