  Recently, it has been shown that compressed sensing (CS) has the potential to
lower energy consumption in wireless electrocardiogram (ECG) systems. By
reducing the number of acquired measurements, the communication burden is
decreased and energy is saved. In this paper, we aim at further reducing the
number of necessary measurements to achieve faithful reconstruction by
exploiting the representational power of restricted Boltzmann machines (RBMs)
to model the probability distribution of the sparsity pattern of ECG signals.
The motivation for using this approach is to capture the higher-order
statistical dependencies between the coefficients of the ECG sparse
representation, which in turn, leads to superior reconstruction accuracy and
reduction in the number of measurements, as it is shown via experiments.
