Electrophysiological signals including electrocardiography (ECG) and electromyography (EMG) are widely used in clinical environments for monitoring of patients and for diagnosis of conditions including cardiac and neuromuscular disease. Due to the wealth of information contained in these signals, many additional applications would be facilitated by full-time acquisition combined with automated analysis. Recent performance gains in portable computing devices and large scale computing platforms provide the necessary computational resources to process and store this data; however challenges at the sensor level have prevented monitoring systems from reaching the practicality and convenience necessary for widespread, continuous use.

In this thesis, we examine the feasibility of applying techniques from the compressive sensing field to the acquisition and analysis of electrophysiological signals. These techniques allow signals to be acquired in compressed form, thereby providing a means to reduce power consumption of monitoring devices. We demonstrate the effects of several methods of compressive sampling and reconstruction on standard compression and reconstruction error metrics. Additionally, we investigate the effects of compressive sensing on the accuracy of automated signal analysis techniques for extracting useful information from ECG and EMG signals.