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Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review

Author

Listed:
  • Giovanna Zimatore

    (Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
    IMM-CNR, 40129 Bologna, Italy)

  • Maria Chiara Gallotta

    (Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, 00185 Roma, Italy)

  • Matteo Campanella

    (Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy)

  • Piotr H. Skarzynski

    (Department of Teleaudiology and Screening, World Hearing Center, Institute of Physiology and Pathology of Hearing, 02-042 Warsaw, Poland
    Heart Failure and Cardiac Rehabilitation Department, Faculty of Medicine, Medical University of Warsaw, 03-042 Warsaw, Poland
    Institute of Sensory Organs, 05-830 Warsaw, Poland)

  • Giuseppe Maulucci

    (Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
    Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy)

  • Cassandra Serantoni

    (Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
    Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy)

  • Marco De Spirito

    (Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
    Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy)

  • Davide Curzi

    (Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy)

  • Laura Guidetti

    (Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy)

  • Carlo Baldari

    (Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy)

  • Stavros Hatzopoulos

    (Clinic of Audiology & ENT, University of Ferrara, 44121 Ferrara, Italy)

Abstract

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

Suggested Citation

  • Giovanna Zimatore & Maria Chiara Gallotta & Matteo Campanella & Piotr H. Skarzynski & Giuseppe Maulucci & Cassandra Serantoni & Marco De Spirito & Davide Curzi & Laura Guidetti & Carlo Baldari & Stavr, 2022. "Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12719-:d:933723
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    References listed on IDEAS

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    Cited by:

    1. Giovanna Zimatore & Cassandra Serantoni & Maria Chiara Gallotta & Laura Guidetti & Giuseppe Maulucci & Marco De Spirito, 2023. "Automatic Detection of Aerobic Threshold through Recurrence Quantification Analysis of Heart Rate Time Series," IJERPH, MDPI, vol. 20(3), pages 1-11, January.
    2. Ping-Yen Lin & Cheng-Ting Tsai & Chang Francis Hsu & Ying-Hsiang Lee & Han-Ping Huang & Chun-Che Huang & Lawrence Yu-Min Liu & Long Hsu & Ten-Fang Yang & Po-Lin Lin, 2022. "The Autonomic Imbalance of Myocardial Ischemia during Exercise Stress Testing: Insight from Short-Term Heart Rate Variability Analysis," IJERPH, MDPI, vol. 19(22), pages 1-12, November.

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