SDNN24 Estimation from Semi-Continuous HR Measures

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

Physiological recovery among workers in long-distance sleddog races: A case study on female veterinarians in Finnmarksløpet

BACKGROUND: During Finnmarksløpet (FL, one of the longest distance sleddog races in the world), veterinarians are exposed to extreme environmental conditions and tight working schedules, with little and fragmented sleep. OBJECTIVE: The aim of this case study was to examine cardiovascular parameters and sleep-wake patterns among veterinarians working within FL, during and after (for a month) the end of the race. METHODS: Six female veterinarians volunteered for the study. The participants wore a wrist device for a total of eight weeks in order to passively and semi-continuously record physiological responses throughout the day (i.e., heart rate, heart rate variability, number of steps, and sleep quality). Moreover, perceived sleep quality was assessed by Pittsburgh Sleep Quality Index (PSQI) at three time-points. RESULTS: During and for one month after completion of the FL, most veterinarians presented an alteration of cardiovascular parameters and sleep quality. The heart rate circadian rhythm returned to pre-race values within about two weeks. CONCLUSIONS: The long-lasting alteration of the veterinarians’ cardiovascular parameters and sleep-wake patterns might have negative consequences for their health in the long-term, especially if similar experiences are repeated more times though the course of a year or season. More research is needed in order to understand the health risks, as well as how to prevent them, among veterinarians in long-distance sleddog races or other similar events.

A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer

In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.