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Abstract

Epilepsy is definitely a significant burden to the world because of its associated health related risks. For an epileptic patient to control and treat the unpredictable occurrence of seizures is quite a hectic task. A fast, efficient and versatile screening process is required that would aid the neurologists to diagnose and understand the treatment of the patient. Electroencephalography (EEG) is used traditionally for the in depth analysis of epilepsy. Since the recordings of the EEG are too long, processing such a huge data is difficult and therefore the dimensions of the data have to be reduced. In this paper, Variational Bayesian Matrix Factorization (VBMF) is employed to reduce the dimensions of the EEG data. The dimensionally reduced data is then transmitted through a Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) System. A Suitable Peak to Average Power Ratio (PAPR) Reduction scheme is engaged to reduce the Bit Error Rate (BER) and PAPR for the MIMO-OFDM System. At the receiver, the paper proposes a particle swarm based sparse representation classifier for the classification of epilepsy from EEG signals. The performance metrics analyzed here are specificity, sensitivity, time delay, quality values, performance index, accuracy, PAPR and BER.




Keywords

EEG

1

Epilepsy




VBMF

Introduction

Epilepsy is one of the neurological disorders which have a terrible socio-economic influence in some people [1]. This type of brain disorders is one in which people have recurring seizures. Due to some minor and temporary changes in the electrical activities of the brain a seizure is produced and it occurs in the cortical regions of the brain [2]. Epileptic attacks results in abnormal sensations and behaviors and the voluntary muscles are contracted involuntarily. The electrical activities of the brain can be easily represented with the

S.K. Prabhakar (&)
H. Rajaguru Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India e-mail: [email protected]




MIMO-OFDM




PAPR

help of Electroencephalography (EEG). The EEG signals provide very rich information regarding the electrical activities of the human brain. Captured from the scalp of human brain, the EEG records the fluctuations happening due to the mental activities. Since the EEG recordings are too long, the entire data cannot be processed and therefore suitable dimensionality reduction techniques are required to reduce the size of the EEG data. The paper is organized as follows: In Sect. 2 the materials and methods are discussed followed by the usage of VBMF as a dimensionality reduction technique. In Sect. 3, the DSTBC MIMO-OFDM System with a reduced PAPR using Partial Transmit Sequence (PTS) is implemented to get a low BER. In Sect. 4, at the receiver side a Particle Swarm Based Sparse Representation Classifier is employed to get a perfect classification rate followed by the results and discussion in

© Springer Nature Singapore Pte Ltd. 2018 T. Vo Van et al. (eds.), 6th International Con

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