Adaptive energy-aware encoding for DWT-based wireless EEG tele-monitoring system

Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography (EEG)-based applications lie at the heart of these promising technologies. However, excess power consumptions may render some of these applications inapplicable. Wireless (EEG) tele-monitoring systems performing encoding and streaming over energy-hungry wireless channels are limited in energy supply. Hence, energy efficient methods are needed to improve such applications. In this work, an embedded EEG encoding system that is able to adjust its computational complexity is proposed; which lead to energy consumption according to channel variations. We analyze the computational complexity for a typical Discrete Wavelet Transform (DWT)-based encoding system. We also propose a power-distortion-compression ratio (P-D-CR) framework. Using the developed P-D-CR framework, the encoder effectively reconfigures the complexity of the control parameters to match the energy constraints while retaining maximum reconstruction quality. Results show that by using the proposed framework, higher reconstruction accuracy can be obtained regardless of the power budget of the utilized hardware.