25/09/2015 CapnoBase.org: Pulse Oximeter IEEE TBME Benchmark

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Download the IEEE TBME Respiratory Rate Benchmark dataset

This zip file contains the pulse oximetry benchmark data set that was used for the validation of theSmartFusion respiratory rate (RR) estimation algorithm presented in IEEE Transactions of BioMedical Engineering (2013) 60(7):194653. The SmartFusion estimates RR from thephotoplethysmogram (PPG) using three respiratory induced variation of the PPG. The data in this setare derived from the capnobase data set.

The data set contains:

raw PPG signal for 42 cases of 8 min duration pulse peak and artifact labels validated by an expert raterreference CO2 signal and derived instantaneous RR for all casesreference ECG signal with R peak and artifact labels, validated by an expert raterreference instantaneous HR derived from ECG and PPG pulse peak labels output of SmartFusion algorithm and subcomponents when applied to this Benchmark data setfor comparison with your algorithm

Check also the README.txt file in the zip file for more information.

Download link will become active when article is published.

25/09/2015 CapnoBase.org: Pulse Oximeter IEEE TBME Benchmark

http://www.capnobase.org/database/pulseoximeterieeetbmebenchmark/ 2/3

Photoplethysmography Respiratory Rate Benchmark Data Set

Karlen_et_al.__2013__Multiparameter_respiratory_rate_estimation.pdf 2.0 M

TBME2013PPGRRBenchmark_R3.zip 26 M

Revision History

11/02/2015 R3:

adds demographics information such as weight, age and ventilation mode to the meta structure

01/05/2013 R2:

fixes erronous reference.hr.pleth that were privously imported from signal that was notcorrected for artifactsadds signal.ecg, labels.ecg, and reference.hr.ecg ; allows to compare hr against ECG

28/04/2013 R1:

initial public release

Scientific work citing the Capnobase IEEE TBME benchmarkdataset

2015 (1)Probabilistic Estimation of Respiratory Rate from Wearable Sensors. Pimentel, M., A., F.; Charlton,P., H.; and Clifton, D., A. Volume 15 of Smart Sensors, Measurement and Instrumentation. 10, pages241262. Springer International Publishing, 2015.

2014 (3)Estimating respiratory and heart rates from the correntropy spectral density of thephotoplethysmogram. Garde, A.; Karlen, W.; Ansermino, J., M.; and Dumont, G., a. PLoS ONE,9(1):e86427. 1 2014. Fuzzy Entropy Based Motion Artifact Detection and Pulse Rate Estimation For FingertipPhotoplethysmography. Paradkar, N.; and Chowdhury, S., R. 5861. 2014. PhotoECG: Photoplethysmographyto estimate ECG parameters. Banerjee, R.; Sinha, A.; Choudhury,A., D.; and Visvanathan, A. In 2014 IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), pages 44044408, 5 2014. IEEE

2013 (2)Multiparameter respiratory rate estimation from the photoplethysmogram. Karlen, W.; Raman, S.;Ansermino, J., M.; and Dumont, G., A. IEEE transactions on biomedical engineering, 60(7):1946

25/09/2015 CapnoBase.org: Pulse Oximeter IEEE TBME Benchmark

http://www.capnobase.org/database/pulseoximeterieeetbmebenchmark/ 3/3

53. 7 2013. Empirical mode decomposition for respiratory and heart rate estimation from thephotoplethysmogram. Garde, A.; Karlen, W.; Dehkordi, P.; Ansermino, J., M.; and Dumont, G. InComputing in Cardiology (CinC), volume 40, pages 799802, 2013. IEEE

Please send us an email if you have published scientific work using this data and we will cite it here.

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