Name
Abstract | ||
|---|---|---|
In this paper, an algorithm for breathing rate extraction from PPG signal is proposed. Two critical aspects have been endorsed during the implementation: i) good performances and ii) low computational complexity. The proposed solution is based on the Empirical Mode Decomposition (EMD) approach and it proves to be robust and accurate even in presence of noisy epochs. It has been validated on two distinct datasets: a)experimental data we have collected using wearables for physiological monitoring and b) recording sessions from PhysioBank MIMIC II Waveform Database. The presented results showed a mean absolute error of 0.0044 Hz, corresponding to 0.26 breaths per minute. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BioCAS.2015.7348369 | 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Keywords | Field | DocType |
photoplethysmographic signal,PhysioBank MIMIC II Waveform Database,empirical mode decomposition,computational complexity,PPG signal,breathing rate extraction | Computer vision,Wearable computer,Physiological monitoring,Computer science,Waveform,Mean absolute error,Respiratory rate,Electronic engineering,Artificial intelligence,Wearable technology,Computational complexity theory,Hilbert–Huang transform | Conference |
ISSN | Citations | PageRank |
2163-4025 | 1 | 0.41 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Fusco | 1 | 1 | 0.41 |
| D. Locatelli | 2 | 1 | 0.41 |
| F. Onorati | 3 | 1 | 0.41 |
| Gianluca Durelli | 4 | 30 | 7.13 |
| Marco D. Santambrogio | 5 | 771 | 91.15 |