Paper Info
Title
Multifuse Multilayer Multikernel Rvfln Plus Of Process Modes Decomposition And Approximate Entropy Data From Ieeg/Seeg Signals For Epileptic Seizure Recognition
Abstract
In this paper, the extracted features using variational mode decomposition (VMD) and approximate entropy (ApEn) privileged information of the input EEG signals are combined with multilayer multikernel random vector functional link network plus (MMRVFLN + ) classifier to recognize the epileptic seizure epochs efficaciously. In our experiment Bonn University single-channel intracranial electroencephalogram (iEEG) and Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multichannel scalp EEG (sEEG) recordings are considered to evaluate the efficacy of the proposed method. The VMD is applied on chaotic, non-stationary, nonlinear, and complex EEG signal to decompose it into three band-limited intrinsic mode functions (BLIMFs). The Hilbert transform (HT) is applied on BLIMFs to extract informative spectral and temporal features. The ApEn is computed from the raw EEG signals as the privileged information and given to the multi-hidden layer structure to obtain the most discriminative compressed form. The scatter plots show the distinct nature of compressed privileged ApEn information among the seizure pattern classes. The linear as well as nonlinear mapping, local and global kernel function, high-learning speed, less computationally complex MMRVFLN+ classifier is proposed to recognize the seizure events accurately by importing the efficacious features with ApEn as the input. The advanced signal processing algorithm i.e., Hilbert Huang transform (HHT) with ApEn and MMRVFLN+ are combined to compare the performance with the proposed VMDHTApEn-MMRVFLN+ method. The proposed method has remarkable recognition ability, superior classification accuracy, and excellent overall performance as compared to other methods. The digital architecture of the multifuse MMRVFLN+ is developed and implemented on a high-speed reconfigurable FPGA hardware platform to validate the effectiveness of the proposed method. The superior classification accuracy, the negligible false positive rate per hour (FPR/h), simplicity, feasibility, robustness, and practicability of the proposed method validate its ability to recognize the epileptic seizure epochs automatically.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2021.104299
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Epileptic seizure, Variational mode decomposition (VMD), Multilayer multikernel random vector, Intracranial electroencephalogram (iEEG), Scalp EEG (sEEG), Hilbert transform (HT), Field-programmable gate array (FPGA)
Journal
132
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Susanta Kumar Rout100.68
Mrutyunjaya Sahani200.68
P K Dash300.34
Pradyut Kumar Biswal400.68