Key result
Technical and physiological uncertainties significantly degrade signal quality and impair heart rate variability analysis.
Why the study?
Uncertainties from data acquisition, computational models, and physiological factors can degrade signal quality and affect HRV analysis, but existing reviews primarily focus on clinical applications, trends, or specific aspects like measurement noise.
Systematic Review
This systematic review provides a holistic overview of uncertainties in HRV methods and their impacts on HRV measures, serving as a reference for developing robust HRV analysis models.
Clinicians should interpret HRV metrics cautiously amid acquisition and model uncertainties; leaves open development of robust mitigation methods.
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.
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Lü et al. (2023) conducted a systematic review in Heart rate variability analysis uncertainties. Heart rate variability analysis was evaluated. Uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and significantly affect heart rate variability analysis.
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