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Method for the Statistical Analysis of the Signals Generated by an Acquisition Card for Pulse Measurement

Author

Listed:
  • Yaquelin Verenice Pantoja-Pacheco

    (Centro de Innovación Aplicada en Tecnologías Competitivas/CIATEC A.C., Omega 201, Industrial Delta, León 37545, Mexico
    Tecnológico Nacional de México/Instituto Tecnológico de Celaya, Celaya 38010, Mexico)

  • Javier Yáñez-Mendiola

    (Tecnológico Nacional de México/Instituto Tecnológico de Celaya, Celaya 38010, Mexico)

Abstract

This article shows a method for the statistical analysis of signals. Firstly, this method was applied to analyze the processing of signs generated by an acquisition card for pulse measurement using the synchronous demodulation method. The application of the method allowed the study of each signal consisting of a descriptive statistical analysis, followed by the analysis of the trend and dynamics of the movement using the augmented Dickey–Fuller test and Hurst exponent, respectively. Secondarily, the method presented here supported the comparison between the pulse signals obtained by synchronous demodulation and plethysmography methods. In addition, the residuals from the pulse comparison of both methods were analyzed. To quantify the differences between the signals, these were compared using the mean-squared error, the root-mean-square error, the mean absolute error, the mean error, the mean absolute percentage error, and the mean percentage error. After this research, it was possible to analyze the signals knowing characteristics such as the following: the presence of normal, exponential, lognormal, and uniform distributions, stationary trend, and dynamic movement anti-persistent. The novelty that this article proposes is the use of concepts traditionally used in the study of time series and models of demand administration, now focused on supporting improvements over the different stages of design and conceptualization of signal processing devices.

Suggested Citation

  • Yaquelin Verenice Pantoja-Pacheco & Javier Yáñez-Mendiola, 2024. "Method for the Statistical Analysis of the Signals Generated by an Acquisition Card for Pulse Measurement," Mathematics, MDPI, vol. 12(6), pages 1-24, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:923-:d:1360947
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    References listed on IDEAS

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    1. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    2. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    3. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
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    Cited by:

    1. Harman Preet Singh & Azira Ab Aziz, 2025. "Impact of intelligent learning assistants on creativity of university students: a self-determination theory perspective," Future Business Journal, Springer, vol. 11(1), pages 1-18, December.

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