FPGA Design of an Efficient EEG Signal Transmission Through 5G Wireless Network Using Optimized Pilot Based Channel Estimation: A Telemedicine Application

Electroencephalogram (EEG) signifies a neurophysiologic measurement, which perceives the electrical activity of brain via making a record of EEG signal from the electrodes positioned on the scalp. With the progression of wired and wireless technologies both m-healthcare and e-healthcare turn into an essential fragment of biomedical science. Mixing of EEG signal with some other biological signal is referred as artifacts. Removal of artifacts postures an abundant challenge in the medical field. In this paper, Hybrid multi resolution discrete wavelet transform based delayed error normalized least mean square is proposed to eradicate motion artifact from the recorded EEG signal. After filtering process Encryption and Encoding take place with chaos encryption and Turbo encoder. Encryption is the process of scrambling the plain EEG signal in to Chipper format. Telemedicine system can be used to transmit medical data transmission and it requires an optimal channel estimation method (ESSA) to reduce BERs. The main aim of ESSA algorithm is to optimally place the pilot symbols and in-order to enable the automatic estimation of state of the channel. Channel estimation is facilitated through GFDM-IM modulation approach and the estimation can be done through SVD-LMMSE module. The proposed optimal based channel estimation is simulated under Xilinx platform with Verilog coding. Then, the performance of the proposed method will be analysed in terms of BER, area and frequency.