Adaptive compression and optimization for real-time energy-efficient wireless EEG monitoring systems

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 this promising technologies. However, excessive power consumption may render some of these applications inapplicable. Hence, intelligent energy efficient methods are needed to improve such applications. In this work, such improved efficiency can be obtained by utilizing smart compression techniques, which reduce airtime over energy-hungry wireless channels; In particular, discrete wavelet transform (DWT) and compressive sensing (CS) are used for EEG signals acquisition and compression. To achieve low-complexity energy-efficient system, the proposed technique makes use of the receiver feedback signals in order to switch between both algorithms based on the application needs. Experimental study has shown that the proposed algorithm effectively reconfigures the utilized compression algorithm parameters based on a channel feed back signal.