Compressed sensing has recently been actively investigated as a mean of lowering the power consumption of sensing nodes in biomedical signal devices due to its capability to reduce the amount of data to be transmitted for the correct reconstruction of the acquired waveforms. The rakeness-based design of compressed sensing stages exploits the uneven distribution of energy in the sensed signal and has proved to be extremely effective in maximizing the energy savings. Yet, many bodyarea sensor network architectures include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this case, the decoderside power consumption is also an issue. In this paper, with particular reference to ECG signals, we show that rakenessbased design is also capable to reduce resources required at the decoder side for reconstruction. This happens across a variety of reconstruction algorithms that see their running time substantially reduced. Actual savings are then experimentally quantified by measuring the energy requirements of one of the algorithms on a common mobile computing platform.