Secure Transmission of EEG Data Using Watermarking Algorithm for the Detection of Epileptical Seizures

Abstract

Internet of things (IoT) has a collection of multiple network-enabled devices like sensors, gateways, smartphones, and communication links (short and long ranges). Tremendous capacity of IoT system has made possible to monitoring and detection of epileptical seizures in real time. For this purpose, various smart devices and applications, helps to transmit information securely. Amalgamation of IoT with healthcare system provides opportunity to deal issues like security, detection of seizures and real time monitoring. The proposed model of cloud-enabled Health IoT system has been presented in this paper, gives the idea about monitoring of epileptical patients. For secured transmission of Electroencephalogram (EEG) data, digital watermarking technique has been used over two dimensional EEG data obtained through one dimensional EEG data by applying Short Time Fourier Transform (STFT). In this paper, watermarking of two dimensional EEG data has been done using discrete wavelet transform - discrete cosine transform (DWT-DCT) based Bacterial Foraging Optimization (BFO) technique and its performance has been figure out. Here, satisfactory watermarking performance in terms of Peak Signal to Noise Ratio (PSNR) 49.50 for class Z and 49.61 for class S EEG data along with Normalized Cross Correlation (NCC) 0.0039 for both classes of EEG data have been achieved.