FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network

Abstract

In this article, an optimized variational mode decomposition (OVMD), reduced deep convolutional neural network (RDCNN), and multi-kernel random vector functional link network (MKRVFLN) are combined to recognize the epileptic seizure using electroencephalogram (EEG) signals. An improved particle swarm optimization based on the maximum value of log energy entropy is introduced to compute the optimized values of the number of band-limited intrinsic mode functions (BLIMFs) and the data-fidelity factor. The Kurtosis and correlation coefficient are used to extract the most efficient BLIMF from highly nonlinear and non-stationary multi-class epilepsy EEG signals. The RDCNN structure is designed to extract the most discriminative unsupervised features and feed into the novel supervised MKRVFLN classifier to train efficiently by reducing the cross-entropy loss for recognizing the seizure epochs efficaciously. The Bonn University, Germany, Neurology and Sleep centre, New Delhi single-channel EEG, and Boston Children’s Hospital multichannel scalp EEG (sEEG) datasets are considered to evaluate the overall efficiency of the proposed method. The proposed method has produced 100% classification accuracy (CA) for classification problem (CP) 1 to 4 present in database-A, as well as for all CP incorporated with database-B and also it has 99.88% CA for CP-5 of the Bonn University database using a ten-fold cross-validation strategy. Furthermore, the proposed method has also the sensitivity of 100%, specificity of 99.34%, and CA of 99.37% with a negligible false positive per hour (FPR/h) of 0.663% by adopting 50% training, 30% testing and 20% validation of total data present in Boston database and its overall performance outperforms as compared to other state-of-the-art methods. The less computational complexity, higher learning speed, accurate epileptic seizure recognition, remarkable classification accuracy, short event recognition time, and negligible FPR/h are the main advantages of the proposed OVMD-RDCNN-MKRVFLN method over RDCNN, OVMD-RDCNN, and OVMD-RDCNN-KRVFLN methods. The novel RDCNN-MKRVFLN digital architecture is designed and implemented on a high-speed field-programmable gate array (FPGA) hardware environment to design a computer-aided-diagnosis (CAD) system for online epileptic seizure diagnosis. The simplicity, feasibility, and practicability of the proposed method validate the effectiveness of automatic seizure recognition.