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Energy efficient compression techniques for biological signals on a sensors node

University of British Columbia

Compression of biological signals is rapidly gaining much attention in research especially for Wireless Body Area Networks (WBANs) applications. This is because of their demonstrated potential in assisting physicians and patients, and helping them achieve a more convenient lifestyle. This work focuses on some problems arising in the deployment of EEG signals in WBANs where EEG data is collected and then transmitted using devices powered by batteries. To elongate the battery life, the energy consumed by acquiring, processing and transmitting the data has to be minimized. Lots of work using Compressed Sensing (CS) have addressed this problem and have demonstrated power savings in WBANs for applications such as Seizure detection. None of these studies however, have demonstrated a high quality signal recovery at high compression ratios such as 10:1. Higher quality signal recovery results in better performance for seizure detection. The ultimate goal is to achieve high quality recovery at high compression rates so as to elongate the battery life and without degrading the performance of WBAN applications. Two frameworks have been previously proposed to solve this problem. The first is a CS framework, which has an Analog to Digital Converter (ADC), micro-controller, and a low power transmitter at the sensor node. The second framework has an under-sampling circuit, an ADC, and a transmitter. This thesis compares the results of the state of the art CS algorithms of both frameworks and demonstrates their performance in automatic seizure detection. Then it proposes two methods that achieve high quality signal recovery at high compression rates, for both frameworks. These methods are demonstrated on 3 different datasets. The first method, BSBL-LNLD is used for the CS framework. It exploits the linear and a non-linear dependency of multivariate EEG signals to recover the compressed signals. This method can achieve up to 0.06 Normalized Mean Square Error (NMSE) at 10:1 compression ratio. The second method solves the under-sampling CS framework using a Meyer wavelet over-complete dictionary by using the Analysis-Prior Formulation. This method achieves up to 0.18 NMSE at 10:1 compression ratio. The proposed methods achieve superb recovery quality and significantly decreased energy consumption.

Text

2017-03-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/61057

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/