Evaluation of compressed sensing in seismocardiogram (SCG) systems

The seismocardiogram (SCG) measures the acceleration generated by the mechanical contraction and relaxation activities of the heart. It has been demonstrated to facilitate accurate identification of various coronary artery diseases. However, applications involving SCG are severely hampered by the large amount of data to be processed, preventing real-time monitoring and detection of diseases. This challenge is exacerbated in the case of tri-axial SCG, with the increase in data collected. Addressing this challenge, compressed sensing (CS) is a promising technique to potentially capture and represent signals significantly below the Nyquist rate. To this end, this work explores the possibility of using CS with SCG systems, by proposing suitable signal processing algorithms, and evaluating these methods with experimental data. The obtained results demonstrate significant reduction in bandwidth, while maintaining accurate signal recovery.