FPGA implementation of DWT EEG data compression for wireless body sensor networks
المؤلف | Elsayed, Mohamed |
المؤلف | Badawy, Ahmed |
المؤلف | Mahmuddin, Massudi |
المؤلف | Elfouly, Tarek |
المؤلف | Mohamed, Amr |
المؤلف | Abualsaud, Khalid |
تاريخ الإتاحة | 2020-08-20T08:06:22Z |
تاريخ النشر | 2017 |
اسم المنشور | 2016 IEEE Conference on Wireless Sensors, ICWiSE 2016 |
المصدر | Scopus |
الملخص | 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. 2016 IEEE. |
راعي المشروع | ACKNOWLEDGMENT This research was made possible by NPRP 6-150-2-059 and NPRP 7-684-1-127 grants from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | ECG EEG FPGA WBSN |
النوع | Conference Paper |
الصفحات | 5-Jan |
رقم المجلد | 2017-December |
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