ScholarLens Back to search
2014•Computer Engineering and Applications JournalOpen access

Higher efficient compressed sensing reconstruction algorithm of PPG signal

Kang Rutin

Open full text 0 citations

Abstract

In this paper, the Compressed Sensing(CS)framework is introduced firstly, and then sparsity of the PhotoPlethysmography(PPG)signal is analyzed. Finally, the PPG signal compression and reconstruction framework based on CS theory is proposed. Via Orthogonal Matching Pursuit(OMP)and Enhanced Orthogonal Matching Pursuit(E-OMP), it is demonstrated that the performance of reconstruction is correlated with the length of the signal, the compression ratio and the number of measurements.

About this research paper

What this paper is about

In this paper, the Compressed Sensing(CS)framework is introduced firstly, and then sparsity of the PhotoPlethysmography(PPG)signal is analyzed. Finally, the PPG signal compression and reconstruction framework based on CS theory is proposed. Via Orthogonal Matching Pursuit(OMP)and Enhanced Orthogonal Matching Pursuit(E-OMP), it is demonstrated that the performance of reconstruction is correlated with the length of the signal, the compression ratio and the number of measurements.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In this paper, the Compressed Sensing(CS)framework is introduced firstly, and then sparsity of the PhotoPlethysmography(PPG)signal is analyzed. Finally, the PPG signal compression and reconstruction framework based on CS theory is proposed. Via Orthogonal Matching Pursuit(OMP)and Enhanced Orthogonal Matching Pursuit(E-OMP), it is demonstrated that the performance of reconstruction is correlated with the length of the signal, the compression ratio and the number of measurements.

Key concepts: Matching pursuit, Compressed sensing, Signal reconstruction, SIGNAL (programming language), Photoplethysmogram, Computer science, Compression (physics), Algorithm

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Higher efficient compressed sensing reconstruction algorithm of PPG signal — Research Paper | ScholarLens