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The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future

Author

Listed:
  • Cristina Rueda

    (Department of Statistics and Operations Research, Universidad de Valladolid, 47011 Valladolid, Spain)

  • Itziar Fernández

    (Department of Statistics and Operations Research, Universidad de Valladolid, 47011 Valladolid, Spain)

  • Yolanda Larriba

    (Department of Statistics and Operations Research, Universidad de Valladolid, 47011 Valladolid, Spain)

  • Alejandro Rodríguez-Collado

    (Department of Statistics and Operations Research, Universidad de Valladolid, 47011 Valladolid, Spain)

Abstract

Oscillatory systems arise in the different biological and medical fields. Mathematical and statistical approaches are fundamental to deal with these processes. The Frequency Modulated Mobiüs approach (FMM), reviewed in this paper, is one of these approaches. Little known as it has been recently developed, it solves a variety of exciting questions with real data; some of them, such as the decomposition of the signal into components and their multiple uses, are of general application, others are specific. Among the exciting specific applications is the automatic interpretation of the electrocardiogram signal. In this paper, a summary of the theoretical, statistical and computational properties of the FMM approach are revised. Additionally, as a novelty, the FMM approach’s usefulness for the analysis of blood pressure signals is shown. For the latter, a new robust estimation algorithm is proposed using FMM models with restrictions. The paper ends with a view about challenges for the future.

Suggested Citation

  • Cristina Rueda & Itziar Fernández & Yolanda Larriba & Alejandro Rodríguez-Collado, 2021. "The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1145-:d:557623
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    References listed on IDEAS

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    1. Corinne Teeter & Ramakrishnan Iyer & Vilas Menon & Nathan Gouwens & David Feng & Jim Berg & Aaron Szafer & Nicholas Cain & Hongkui Zeng & Michael Hawrylycz & Christof Koch & Stefan Mihalas, 2018. "Generalized leaky integrate-and-fire models classify multiple neuron types," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    2. Björn Naundorf & Fred Wolf & Maxim Volgushev, 2006. "Unique features of action potential initiation in cortical neurons," Nature, Nature, vol. 440(7087), pages 1060-1063, April.
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