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Diseases 2018, 6(1), 20; https://doi.org/10.3390/diseases6010020

Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms

1
Faculty of Medicine, University of British Columbia, Vancouver, BC V1Y 1T3, Canada
2
BC Children’s & Women’s Hospital, Vancouver, BC V6H 3N1, Canada
3
School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 8 March 2018 / Accepted: 10 March 2018 / Published: 11 March 2018
(This article belongs to the Special Issue Non-invasive Diagnostics for Cardiovascular Diseases)
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Abstract

Photoplethysmogram (PPG) signals collected using a pulse oximeter are increasingly being used for screening and diagnosis purposes. Because of the non-invasive, cost-effective, and easy-to-use nature of the pulse oximeter, clinicians and biomedical engineers are investigating how PPG signals can help in the management of many medical conditions, especially for global health application. The study of PPG signal analysis is relatively new compared to research in electrocardiogram signals, for instance; however, we anticipate that in the near future blood pressure, cardiac output, and other clinical parameters will be measured from wearable devices that collect PPG signals, based on the signal’s vast potential. This article attempts to organize and standardize the names of PPG waveforms to ensure consistent terminologies, thereby helping the rapid developments in this research area, decreasing the disconnect within and among different disciplines, and increasing the number of features generated from PPG waveforms. View Full-Text
Keywords: photoplethysmography; pulse oximeter; clinical parameters; blood pressure estimation; global health; digital health; mobile health photoplethysmography; pulse oximeter; clinical parameters; blood pressure estimation; global health; digital health; mobile health
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Elgendi, M.; Liang, Y.; Ward, R. Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms. Diseases 2018, 6, 20.

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