Key result
Heart rate variability detection during motion situations such as exercise or driving reported accuracy between 59% and 85%, with reduced HRV generally indicating increased morbidity and stress.
Why the study?
Heart rate variability analysis reflects autonomic nervous system function, but there is a need to summarize signal processing and machine learning methods and identify gaps to improve research quality.
Answers are grounded in this paper and cite their sources.
HRV analysis using machine learning can assess health states like stress and morbidity, though detection accuracy during motion remains a challenge requiring further improvement.
Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.
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Ishaque et al. (2023) conducted a review in Heart rate variability. Heart rate variability signal analysis was evaluated on Accuracy of HRV detection in motion. Heart rate variability detection during motion situations such as exercise or driving reported accuracy between 59% and 85%, with reduced HRV generally indicating increased morbidity and stress.
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