Adaptive Energy-Aware Encoding for DWT-Based Wireless EEG Monitoring System
Wireless ElectroencephaloGraphy (EEG) tele-monitoring systems performing encoding and streaming over energy-hungry wireless channels are limited in energy supply. However, excessive power consumption either in encoding or radio channels may render some applications inapplicable. Hence, energy efficient methods are needed to improve such applications. In this paper, an embedded EEG encoding system should be able to adjust its computational complexity, hence, energy consumption according to the channel variations. To analyze the distortion-compression ratio (PRD-CR) behavior of the wireless EEG system under energy constraints, both encoding and transmission power should be taken into consideration. In this paper, the authors propose a Power-Distortion-Compression Ratio (P-PRD-CR) framework, which extends the traditional PRD-CR to P-PRD-CR model.