Trends in Heart-Rate Variability Signal Analysis

Author: Syem Ishaque1, Naimul Khan1, Sri Krishnan1
1 Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON, Canada.
Conference/Journal: Front Digit Health
Date published: 2021 Feb 25
Other: Volume ID: 3 , Pages: 639444 , Special Notes: doi: 10.3389/fdgth.2021.639444. , Word Count: 253

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.

Keywords: drowsiness; exercise; heart rate variability; machine learning; morbidity; stress; wireless sensors.

PMID: 34713110 PMCID: PMC8522021 DOI: 10.3389/fdgth.2021.639444