Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals

In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN) are combined to recognize the epileptic seizure using both the multichannel scalp and single-channel electroencephalogram (EEG) signals. The novel RDCSAE structure...

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Vydáno v:IEEE transactions on biomedical circuits and systems Ročník 15; číslo 3; s. 595 - 605
Hlavní autoři: Sahani, Mrutyunjaya, Rout, Susanta Kumar, Dash, Pradipta Kishor
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Algorithms
Artificial neural networks
CAD
Computer aided design
Computer applications
Convolution
Convulsions & seizures
Cost function
Diagnosis
EEG
Electroencephalography
Epilepsy
Epileptic seizure
Feature extraction
Field programmable gate arrays
improved kernel random vector functional link network
Kernel
Kernels
multichannel scalp electroencephalogram
Neural networks
Recognition
reduced deep convolutional stack autoencoder
Scalp
Seizures
single-channel electroencephalogram
Support vector machines
Wavelet packets
ISSN:1932-4545, 1940-9990, 1940-9990
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Popis
Shrnutí:In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN) are combined to recognize the epileptic seizure using both the multichannel scalp and single-channel electroencephalogram (EEG) signals. The novel RDCSAE structure is designed to extract the most discriminative unsupervised features from EEG signals and fed into the proposed supervised IKRVFLN classifier to train efficiently by reducing the mean-square error cost function for recognizing the epileptic seizure activity with promising accuracy. The proposed RDCSAE-IKRVFLN algorithm is tested over the benchmark Boston Children's Hospital multichannel scalp EEG (sEEG) and Boon University, Germany single-channel EEG databases. The less computational complexity, higher learning speed, better model generalization, accurate epileptic seizure recognition, remarkable classification accuracy, negligible false positive rate per hour (FPR/h) and short event recognition time are the main advantages of the proposed RDCSAE-IKRVFLN method over reduced deep convolutional neural network (RDCNN), RDCSAE and RDCSAE-KRVFLN methods. The proposed RDCSAE-IKRVFLN method is implemented in a high-speed reconfigurable field-programmable gate array (FPGA) hardware environment to design a computer-aided-diagnosis (CAD) system for automatic epileptic seizure diagnosis. The simplicity, feasibility, and practicability of the proposed method validate the stable and reliable performances of epilepsy detection and recognition.
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ISSN:1932-4545
1940-9990
1940-9990
DOI:10.1109/TBCAS.2021.3090995