Name
Abstract | ||
|---|---|---|
Motion Artifact (MA) reduction is an important part in Photoplethysmography (PPG) signal processing for wearing devices. The heavily-corrupted period in PPG can hardly be rebuilt by frequency domain methods. This paper proposes an advanced time-frequency analysis method based on Empirical Mode Decomposition (EMD) using variance characterization of extrema. In this way, the computing costs are largely decreased by picking out the corrupted period, while the wave clusters found in it for estimation can help reduce error detection rate. The result shows our method is accurate in pulse rate estimation for heavily-corrupted PPG signals. The average relative error of our method is 1.03%, which is a result of data from PhysioBank MIMIC II waveform database. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BioCAS.2016.7833765 | 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Keywords | Field | DocType |
PPG,motion artifact,EMD,variance characterization | Frequency domain,Signal processing,Computer vision,Photoplethysmogram,Computer science,Waveform,Error detection and correction,Maxima and minima,Artificial intelligence,Approximation error,Hilbert–Huang transform | Conference |
ISSN | ISBN | Citations |
2163-4025 | 978-1-5090-2960-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Pang | 1 | 5795 | 451.00 |
| Ming Liu | 2 | 0 | 0.68 |
| zhang xu | 3 | 56 | 7.68 |
| Peng Li | 4 | 91 | 9.61 |
| Zhaolin Yao | 5 | 0 | 0.34 |
| Xiaohui Hu | 6 | 82 | 7.59 |
| Hongda Chen | 7 | 99 | 20.06 |
| Qi Gong | 8 | 86 | 14.17 |