Trends in Heart-Rate Variability Signal Analysis
Autor: | Naimul Mefraz Khan, Syem Ishaque, Sridhar Krishnan |
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Rok vydání: | 2023 |
Předmět: |
Cardiac function curve
medicine.medical_specialty Heartbeat lcsh:Medicine morbidity Review Pupil lcsh:QA75.5-76.95 03 medical and health sciences stress 0302 clinical medicine Photoplethysmogram Internal medicine medicine Heart rate variability 030304 developmental biology 0303 health sciences Signal processing exercise business.industry lcsh:Public aspects of medicine lcsh:R heart rate variability drowsiness lcsh:RA1-1270 wireless sensors Autonomic nervous system machine learning Blood pressure Cardiology Digital Health lcsh:Electronic computers. Computer science business 030217 neurology & neurosurgery circulatory and respiratory physiology |
Zdroj: | Frontiers in Digital Health, Vol 3 (2021) Frontiers in Digital Health |
Popis: | 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. |
Databáze: | OpenAIRE |
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