An efficient compressive sensing method for connected health applications

The sensitive domain of healthcare intensifies the shortcomings associated with internet of things (IoT) based remote health monitoring systems in terms of their high-energy consumption and big data issues such as latency and privacy, caused by, the continuous stream of raw data. Hence, in the development of their remote elderly monitoring system (REMS), the authors focus on using embedded multicore architectures as powerful IoT edge devices and energy efficient signal acquisition and processing techniques to elevate such limitations. This study addresses the design of sparsifying matrices for electroencephalogram (EEG) signals in the context of compressed sensing. These signals are known to be non-sparse in both time and standard transform domains. The designed matrices are adapted to the data and are based on the autoregressive modeling of the signal and the singular value decomposition (SVD) of the impulse response matrix of the linear predictive coding (LPC) filter. To facilitate the hardware implementation and to prolong the life of the wearable node, the measurement matrix is chosen to be binary. The proposed algorithm has been applied to the EEGLab dataset ‘eeglab data set’ with an average normalized mean square error of 0.068.