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

Content Provider	IEEE Xplore Digital Library
Author	Tingting Zhu Pimentel, M.A.F. Clifford, G.D. Clifton, D.A.
Copyright Year	2015
Description	Author affiliation: Depts. of Biomed. Inf. & Biomed. Eng., Emory Univ. & Georgia Inst. of Technol., Atlanta, GA, USA (Clifford, G.D.) \|\| Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK (Tingting Zhu; Pimentel, M.A.F.; Clifton, D.A.)
Abstract	Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive RR estimation suffer from a lack of robustness, resulting in the fact that they are not used in clinical practice. We propose a Bayesian approach to fusing the outputs of many RR estimation algorithms to improve the overall robustness of the resulting estimates. Our method estimates the accuracy of each algorithm and jointly infers the fused RR estimate in an unsupervised manner, with aim of producing a fused estimate that is more accurate than any of the algorithms taken individually. This approach is novel in the literature, where the latter has so far concentrated on attempting to produce single algorithms for RR estimation, without resulting in systems that have penetrated into clinical practice. A publicly-available dataset, Capnobase, was used to validate the performance of our proposed model. Our proposed methodology was compared to the best-performing individual algorithm from the literature, as well as to the results of using common fusing methodologies such as averaging, median, and maximum likelihood (ML). Our proposed methodology resulted in a mean-absolute-error (MAE) of 1.98 breaths per minute (bpm), outperformed other fusing strategies (mean fusion: 2.95 bpm; median fusion: 2.33 bpm; ML: 2.30 bpm). It also outperformed the best single algorithm (2.39 bpm) and the benchmark algorithm proposed for use with Capnobase (2.22 bpm). We conclude that the proposed fusion methodology can be used to combine RR estimates from multiple sources derived from the PPG, to infer a reliable and robust estimation of the respiratory rate in an unsupervised manner.
Starting Page	6138
Ending Page	6141
File Size	822461
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319793
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency modulation Standards Robustness Bayes methods Maximum likelihood estimation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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