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Data Reduction Methodology for Dynamic Characteristic Extraction in Photoplethysmogram.

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Titel: Data Reduction Methodology for Dynamic Characteristic Extraction in Photoplethysmogram.
Autoren: Sviridova, Nina, Okazaki, Sora
Quelle: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6232, 28p
Schlagwörter: PHOTOPLETHYSMOGRAPHY, DATA reduction, SIGNAL sampling, PATIENT monitoring, NONLINEAR analysis
Abstract: Photoplethysmogram (PPG) signals are increasingly utilized in wearable and mobile healthcare applications due to their non-invasive nature and ease of use in measuring physiological parameters, such as heart rate, blood pressure, and oxygen saturation. Recent advancements have highlighted green-light photoplethysmogram (gPPG) as offering superior signal quality and accuracy compared to traditional red-light photoplethysmogram (rPPG). Given the deterministic chaotic nature of PPG signals' dynamics, nonlinear time series analysis has emerged as a powerful method for extracting health-related information not captured by conventional linear techniques. However, optimal data conditions, including appropriate sampling frequency and minimum required time series length for effective nonlinear analysis, remain insufficiently investigated. This study examines the impact of downsampling frequencies and reducing time series lengths on the accuracy of estimating dynamical characteristics from gPPG and rPPG signals. Results demonstrate that a sampling frequency of 200 Hz provides an optimal balance, maintaining robust correlations in dynamical indices while reducing computational load. Furthermore, analysis of varying time series lengths revealed that the dynamical properties stabilize sufficiently at around 170 s, achieving an error of less than 5%. A comparative analysis between gPPG and rPPG revealed no significant statistical differences, confirming their similar effectiveness in estimating dynamical properties under controlled conditions. These results enhance the reliability and applicability of PPG-based health monitoring technologies. [ABSTRACT FROM AUTHOR]
Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Data Reduction Methodology for Dynamic Characteristic Extraction in Photoplethysmogram.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Sviridova%2C+Nina%22">Sviridova, Nina</searchLink><br /><searchLink fieldCode="AR" term="%22Okazaki%2C+Sora%22">Okazaki, Sora</searchLink>
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  Data: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6232, 28p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22PHOTOPLETHYSMOGRAPHY%22">PHOTOPLETHYSMOGRAPHY</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+reduction%22">DATA reduction</searchLink><br /><searchLink fieldCode="DE" term="%22SIGNAL+sampling%22">SIGNAL sampling</searchLink><br /><searchLink fieldCode="DE" term="%22PATIENT+monitoring%22">PATIENT monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22NONLINEAR+analysis%22">NONLINEAR analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Photoplethysmogram (PPG) signals are increasingly utilized in wearable and mobile healthcare applications due to their non-invasive nature and ease of use in measuring physiological parameters, such as heart rate, blood pressure, and oxygen saturation. Recent advancements have highlighted green-light photoplethysmogram (gPPG) as offering superior signal quality and accuracy compared to traditional red-light photoplethysmogram (rPPG). Given the deterministic chaotic nature of PPG signals' dynamics, nonlinear time series analysis has emerged as a powerful method for extracting health-related information not captured by conventional linear techniques. However, optimal data conditions, including appropriate sampling frequency and minimum required time series length for effective nonlinear analysis, remain insufficiently investigated. This study examines the impact of downsampling frequencies and reducing time series lengths on the accuracy of estimating dynamical characteristics from gPPG and rPPG signals. Results demonstrate that a sampling frequency of 200 Hz provides an optimal balance, maintaining robust correlations in dynamical indices while reducing computational load. Furthermore, analysis of varying time series lengths revealed that the dynamical properties stabilize sufficiently at around 170 s, achieving an error of less than 5%. A comparative analysis between gPPG and rPPG revealed no significant statistical differences, confirming their similar effectiveness in estimating dynamical properties under controlled conditions. These results enhance the reliability and applicability of PPG-based health monitoring technologies. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/s25196232
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 6232
    Subjects:
      – SubjectFull: PHOTOPLETHYSMOGRAPHY
        Type: general
      – SubjectFull: DATA reduction
        Type: general
      – SubjectFull: SIGNAL sampling
        Type: general
      – SubjectFull: PATIENT monitoring
        Type: general
      – SubjectFull: NONLINEAR analysis
        Type: general
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      – TitleFull: Data Reduction Methodology for Dynamic Characteristic Extraction in Photoplethysmogram.
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            NameFull: Sviridova, Nina
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            – D: 01
              M: 10
              Text: Oct2025
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              Y: 2025
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