FPGA implementation of DWT EEG data compression for wireless body sensor networks

Abstract

Wireless body sensor networks (WBSN) provide an appreciable aid to patients who require continuous care and monitoring. One key application of WBSN is mobile health (mHealth) for continuous patient monitoring, acquiring vital signs e.g. EEG, ECG, etc. Such monitoring devices are doomed to be portable, i.e., batter powered, and agile to allow for patient mobility, while providing sustainable, energy-efficient hardware platforms. Hence, EEG data compression is critical in reducing the transmission power, hence increase the battery life. In this paper, we design and implement a complete hardware model based on discrete wavelet transform (DWT) for vital signs data compression and reconstruction on a field programmable gate array (FPGA) based platform. We evaluate the performance of our DWT compression FPGA implementation under different practical parameters including filter length and the compression ratio. We investigate the hardware and computational complexity of our design in terms of used resource blocks for future comparison with state-of-the-art techniques. Our results show the efficiency of the proposed hardware compression and reconstruction model at different system parameters, including the high pass filter coefficients, and DWT type, and DWT threshold.