Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review
| Autor: | Giovanna Zimatore, Maria Chiara Gallotta, Matteo Campanella, Piotr H. Skarzynski, Giuseppe Maulucci, Cassandra Serantoni, Marco De Spirito, Davide Curzi, Laura Guidetti, Carlo Baldari, Stavros Hatzopoulos |
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| Rok vydání: | 2022 |
| Předmět: |
nonlinear dynamic
Time Factors Health Toxicology and Mutagenesis heart rate variability Public Health Environmental and Occupational Health Poincaré plot recurrence quantification analysis Settore FIS/07 - FISICA APPLICATA (A BENI CULTURALI AMBIENTALI BIOLOGIA E MEDICINA) wearable devices metabolic threshold Heart Rate Physical Fitness Exercise Test sport Exercise |
| Zdroj: | International journal of environmental research and public health. 19(19) |
| ISSN: | 1660-4601 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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