Version 1 - September 2017
When referencing this material, please cite:
@article{OSV,
author = {Amar S. Bhogal and Ali R. Mani},
title = {Pattern Analysis of Oxygen Saturation Variability in Healthy Individuals: Entropy of Pulse Oximetry Signals Carries Information about Mean Oxygen Saturation},
journal = {Frontiers in Physiology},
volume = {8},
issue = {1},
pages = {555},
year = {2017}
}
Please also include the standard citation for PhysioNet:
@article{PhysioNet,
author = {Goldberger, Ary L. and Amaral, Luis A. N.
and Glass, Leon and Hausdorff, Jeffrey M.
and Ivanov, Plamen Ch. and Mark, Roger G.
and Mietus, Joseph E. and Moody, George B.
and Peng, Chung-Kang and Stanley, H. Eugene},
title = {{PhysioBank}, {PhysioToolkit}, and {PhysioNet}:
Components of a New Research Resource for Complex
Physiologic Signals},
journal = {Circulation},
publisher = {American Heart Association, Inc.},
volume = {101},
number = {23},
year = {2000},
month = {June},
pages = {e215--e220},
doi = {10.1161/01.CIR.101.23.e215},
issn = {0009-7322},
url = {http://circ.ahajournals.org/content/101/23/e215}
}
This database contains one hour oxygen saturation measurements of 36 patients, used for the analysis of oxygen saturation variability.
Background
Pulse oximetry is routinely used for monitoring patients' oxygen saturation levels with little regard to the variability of this physiological variable. There are few published studies on oxygen saturation variability (OSV), with none describing the variability and its pattern in a healthy adult population. The aim of this study was to characterise the pattern of OSV using several parameters: the regularity (sample entropy analysis), the self-similarity (detrended fluctuation analysis (DFA)), and the complexity (multiscale entropy (MSE) analysis). Secondly, to determine if there were any changes that occur with age.
The study population consisted of 36 individuals. The 'young' population consisted of 20 individuals [Mean age = 21.0 (SD = 1.36 years)] and the 'old' population consisted of 16 individuals [Mean age = 50.0 (SD = 10.4 years)]. Through DFA analysis, OSV was shown to exhibit fractal-like patterns. The sample entropy revealed the variability to be more regular than heart rate variability and respiratory rate variability. There was a significant inverse correlation between mean oxygen saturation and sample entropy in healthy individuals. Additionally, the MSE analysis described a complex fluctuation pattern, which was reduced with age (p < 0.05). These findings suggest partial "uncoupling" of the cardio-respiratory control system that occurs with ageing. Overall, this study has characterized OSV using pre-existing tools. We have showed that entropy analysis of pulse oximetry signals carries information about body oxygenation. This may have the potential to be used in clinical practice to detect differences in diseased patient subsets.
Data Collection
Before starting the recording, the participants are interviewed to obtain:
- Age
- BMI (use their weight and height on the NHS BMI calculator tool)
- Gender
- Smoking history and/or current smoking status
- Any significant medical conditions that could affect reading
Measurement Setup:
- In the LabChart software, switch off all input sources except input 1,2, and 3.
- Set the sampling frequency to 1KHz.
- Plug Pulse Oximeter into Power Lab input 1.
- Plug the Pulse pressure transducer into Power Lab input 2.
- Attach Respiratory band into Power Lab input 3.
- Connect personal computer to the PowerLab data acquisition system.
Procedure for recording:
- Clean the pulse oximeter and place on finger of participants choosing
- Place the pulse pressure transducer on the adjacent finger
- Wrap the Respiratory band around the umbilicus of the participant
- Preferably have participant sitting with the fingers relatively still
- Test equipment to ensure correct readings
- Once the equipment has been checked stop the test and start the official recording
- Add a comment to show when the data collection has started and once again when it has ended
- After the hour has passed stop the recording, then remove and clean the equipment
- Save the file ensuring complete anonymity by using the date of collection (i.e if 1st participant on January 1st 2017, then save file as 010117A)
Extracting Oxygen Saturation Data for Analysis:
- Select the 1 hour recorded segment
- File > Export As – select LabChart Text FIle
- Choose channel 1 and select the option for current selection
- Down sample by 1000 and remove comments
- Save file in a separate file with the other samples
Data Files
The oxygen saturation data files are provided in standard WFDB format. The sampling frequency of the measurements is 1Hz as specified in the header files.
The participants.csv file contains metadata about each participant.
Contributors
This data was contributed by Amar S. Bhogal and Ali R. Mani from the UCL Division of Medicine, University College London.
Name Last modified Size Description
Parent Directory -
150317B.dat 2017-09-27 12:07 7.0K digitized signal(s)
051216A.hea 2017-09-27 12:07 146 header file
matlab-script.m 2017-09-27 12:07 738
RECORDS 2017-09-27 12:07 288 list of record names
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PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.
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