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Article

Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics

1
Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo 125-8585, Japan
2
International Research Center for Neurointelligence, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-0033, Japan
3
Faculty of Agro-Food Science, Niigata Agro-Food University, 2416 Hiranedai, Tainai 959-2702, Japan
4
Innovation Management Organization, Chiba University, Kashiwano-ha Campus 6-2-1, Kashiwano-ha, Kashiwa-shi 277-0882, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Marc Brecht
Sensors 2022, 22(14), 5154; https://doi.org/10.3390/s22145154
Received: 8 June 2022 / Revised: 3 July 2022 / Accepted: 7 July 2022 / Published: 9 July 2022
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of photoplethysmograms. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated with the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as determinism and entropy can be estimated with an error lower than 1% even for short photoplethysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed. View Full-Text
Keywords: photoplethysmogram; nonlinear dynamics; nonlinear time series analysis; data length assessment photoplethysmogram; nonlinear dynamics; nonlinear time series analysis; data length assessment
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MDPI and ACS Style

Sviridova, N.; Zhao, T.; Nakano, A.; Ikeguchi, T. Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics. Sensors 2022, 22, 5154. https://doi.org/10.3390/s22145154

AMA Style

Sviridova N, Zhao T, Nakano A, Ikeguchi T. Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics. Sensors. 2022; 22(14):5154. https://doi.org/10.3390/s22145154

Chicago/Turabian Style

Sviridova, Nina, Tiejun Zhao, Akimasa Nakano, and Tohru Ikeguchi. 2022. "Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics" Sensors 22, no. 14: 5154. https://doi.org/10.3390/s22145154

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