Biomedical Signals Reconstruction Under the Compressive Sensing Approach

TL;DR

This paper explores the use of compressive sensing techniques to accurately reconstruct biomedical signals like ECG and MRI from limited samples, emphasizing the importance of maintaining signal quality in health monitoring.

Contribution

It demonstrates the application of compressive sensing and optimization algorithms for biomedical signal reconstruction, validated through experimental results.

Findings

Successful reconstruction of biomedical signals from limited samples

Validation of compressive sensing effectiveness in medical signal recovery

Maintained signal quality comparable to full-sample methods

Abstract

The paper analyses the possibility to recover different biomedical signals if limited number of samples is available. Having in mind that monitoring of health condition is done by measuring and observing key parameters such as heart activity through electrocardiogram or anatomy and body processes through magnetic resonance imaging, it is important to keep the quality of the reconstructed signal as better as possible. To recover the signal from limited set of available coefficients, the Compressive Sensing approach and optimization algorithms are used. The theory is verified by the experimental results.