An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation. (February 2022)

• Record Type: Journal Article
• Authors: Rout, Susanta Kumar; Sahani, Mrutyunjaya; Dora, Chinmayee; Biswal, Pradyut Kumar; Biswal, Birendra
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: Electroencephalogram (EEG) signal -- Empirical wavelet transform (EWT) -- Multi-fuse reduced deep convolutional neural network (MF-RDCNN) -- Epileptic seizure classification -- Digital implementation -- Field-programmable gate array (FPGA); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103281
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Epilepsy, an incurable brain disorder portrayed by seizures, is the most common neurological disease worldwide. The embryonic detection of epileptic action helps the psychologist for the diagnosis of epileptic seizure and reduces the seizure effect on the patient's life. Empirical wavelet transform (EWT), and a novel multi-fuse reduced deep convolutional neural network (MF-RDCNN) classifier are integrated to design a computer-aided-diagnosis (CAD) system for meticulous classification of epileptic seizure from electroencephalogram (EEG) signals. The EWT is enforced on recorded EEG signals to extract three distinct band-limited modes (BLMs) and the proposed classifier is used to compute the most informative unsupervised signatures automatically by taking the extracted BLMs as inputs. The experimental results are carried out using three EEG databases provided by Bonn-University, Germany single-channel EEG (dataset-A), Neurology and Sleep Center, New Delhi single-channel EEG (dataset-B), and Boston Children's Hospital multichannel scalp EEG (dataset-C) to verify the effectiveness of the proposed algorithm. The performance of the proposed method is superior over other prevalent methods and has 100 %, 99.82 % overall mean classification accuracy for two class and three class classification problems respectively using ten-fold cross-validation. Moreover, the proposed method has specificity (SPE) of 99.29 %, sensitivity (SEN) of 99.86 %, and classification accuracy (ACC) of 99.29 % with 0.71 % of false positive rate per hour (FPR/h) by taking 40 % of data for training, 40 % of data for testing, and the remaining 20 % of data for validation from the total data incorporated with the database-C. The lesser computational complexity, higher learning speed, short event recognition time, remarkable overall mean classification accuracy, and outstanding overall performance are the major advantages of the proposed EWT-MF-RDCNN method over EWT-RDCNN and RDCNN method for accurate classification of seizure EEG epochs. The digital architecture of the MF-RDCNN classifier is also implemented in a high-speed FPGA processor to develop a CAD system for efficacious diagnosis of epileptic seizure activity online.