Assessment of seven reconstruction methods for contemporary compressive sensing

摘要

In order to acquire signal data without any loss, according to Nyquist rate theorem, the sampling rate must be equal to or more than twice the bandwidth. However, this will result in occupying more memory space and consume more active power at higher sampling rates, which are not suitable for Internet of Things (IoT) applications that have stringent memory and power constraints. Compressive Sensing or Sampling (CS) is a compressing technique that can be used to capture the data at significantly lower rate. This paper presents the simulation results of many contemporary research work on CS for ECG signals. Two CS methods have been studied: pre-processing then compression and under-sampling. Additionally, seven common reconstruction algorithms have been addressed. The simulation results of these CS reconstruction techniques are presented in addition to many metrics that were used to evaluate the performance and quality of reconstruction.