Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn 2015-November, Issue , 2015, Pages 6138-6141

  Zhu, Tingting ^a   Pimentel, Marco A F ^a   Clifford, Gari D ^b   Clifton, David A ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; AGED; ALGORITHM; BAYES THEOREM; BREATHING; BREATHING RATE; CHILD; HUMAN; INFANT; MIDDLE AGED; OXIMETRY; PHOTOELECTRIC PLETHYSMOGRAPHY; PHYSIOLOGY; PRESCHOOL CHILD; THEORETICAL MODEL; TRANSCUTANEOUS OXYGEN MONITORING;

ADOLESCENT; ADULT; AGED; ALGORITHMS; BAYES THEOREM; BLOOD GAS MONITORING, TRANSCUTANEOUS; CHILD; CHILD, PRESCHOOL; HUMANS; INFANT; MIDDLE AGED; MODELS, THEORETICAL; OXIMETRY; PHOTOPLETHYSMOGRAPHY; RESPIRATION; RESPIRATORY RATE;

EID: 84953266881     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EMBC.2015.7319793     Document Type: Conference Paper

References (12)

1. L. Tarassenko and D. A. Clifton, Semiconductor Wireless Technology for Chronic Disease Management, Electronics Letters, s30, 2011.
2. World Health Organization: Fourth Programme Report, 1988-1989: ARI Programme for Control of Acute Respiratory Infections. Technical report, WHO, Geneva, 1990.
3. World Health Organization: IMCI technical seminar on Acute Respiratory Infection, 2001.
4. W. Karlen, S. Raman, J. M. Ansermino, and G. A. Dumont, Multiparameter respiratory rate estimation from the photoplethysmogram, IEEE Transactions on Biomedical Engineering, vol. 60, no. 7, pp. 1946-53, 2013.
5. L. Tarassenko, M. Villarroel, A. Guazzi, J. Jorge, D. A. Clifton, and C. Pugh, Non-contact video-based vital sign monitoring using ambient light and auto-regressive models, Physiological Measurement, vol. 35, no. 5, pp. 807-831, 2014.
6. C. Orphanidou, D. A. Clifton, S. Khan, M. Smith, J. Feldmar, and L. Tarassenko, Telemetry-Based Vital Sign Monitoring for Ambulatory Hospital Patients, IEEE EMBC, pp. 4650-4653, 2009.
7. C. Orphanidou, S. Fleming, S. A. Shah, L. Tarassenko, Data fusion for estimating respiratory rate from a single-lead ECG, Biomedical Signal Processing and Control, vol. 8, no. 1, pp. 98-105, 2013.
8. A. P. Dawid, A. M. Skene, Maximum likelihood estimation of observer error-rates using the EM algorithm, Journal of the Royal Statistical Society Series C Applied Statistics, vol. 28, no. 1, pp. 20-28, 1979.
9. V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, L. Moy, Learning from crowds, Journal of Machine Learning Research, pp. 1297-1322, 2010.
10. T. Zhu, N. Dunkley, J. Behar, D. A. Clifton, G. D. Clifford, Fusing Continuous-Valued Medical Labels using a Bayesian Model, 2015. arXiv:1503. 06619
11. W. Karlen, M. Turner, E. Cooke, G. A. Dumont, and J. M. Ansermino, Capnobase: Signal database and tools to collect, share and annotate respiratory signals, Annual Meeting of the Society for Technology in Anesthesia, 2010.
12. B. N. Li, M. C. Dong, M. I. Vai, On an automatic delineator for arterial blood pressure waveforms, Biomedical Signal Processing and Control, vol. 5, no. 1, pp. 76-81, 2010.