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

Energy-efficient on-board processing technique for wireless epileptic seizure detection systems

“…Given the architecture depicted in Fig. 2 branch (d), power model given in (10), and simulation parameters in table I, the total power consumption and classification accuracy have been measured for each combination individually.…”

“…DWT and CS are the most common compression paradigms that have been utilized in the context of EEG compression. The power models of their encoding systems have been previously addressed in the previous work [10]- [12], however, this paper extends these models to encompass the multi-channel monitoring systems and compare them with the proposed energy-efficient on-board one. 1) Discrete Wavelet Transform (DWT): Wavelet thresholding compression methods deals with wavelet coefficients using a suitable chosen threshold value in advance.…”

“…With the aid of Fig. 2 branch (b) and the power model in [10], the DWT-based system power consumption (P DW T ) is now modified to…”

Lossy compression techniques for EEG signals

“…Taking the advantages of the development of semiconducting materials, author in [8] successfully built the EEG sensor package with size of 40x25mm 2 , weight less than 100g. In addition, authors in [5,7,28,35] proved the feasibility of applying their compressed techniques in wireless EEG monitoring systems and AEEG.…”

“…In particular, EEG signal emerges as an efficient signal that can be utilized to predict brain diseases, epilepsy and sleep distortion. With a modern wearable device, EEG signal could be uninterruptedly recorded up to 14 days [1], which has promoted the widespread use of EEG applications such as Ambulance EEG monitoring (AEEG), telemedicine, braincomputer interface (BCI) [2][3][4][5][6][7][8], and provided patients a costeffective way to monitor their health in real-time.…”