Instantaneous Heart Rate-based Automated Monitoring of Hypertension using Machine Learning

Abstract Background Hydrotherapeutic applications to the head and spine have shown to improve cardiovascular and autonomic functions. There is lack of study reporting the effect of either neutral spinal bath (NSB) or neutral spinal spray (NSS). Hence, the present study was conducted to evaluate and compare the effects of both NSB and NSS in healthy volunteers. Methods Thirty healthy subjects were recruited and randomized into either neutral spinal bath group (NSBG) or neutral spinal spray group (NSSG). A single session of NSB, NSS was given for 15 min to the NSBG and NSSG, respectively. Assessments were taken before and after the interventions. Results Results of this study showed a significant reduction in low-frequency (LF) to high-frequency (HF) (LF/HF) ratio of heart rate variability (HRV) spectrum in NSBG compared with NSSG (p=0.026). Within-group analysis of both NSBG and NSSG showed a significant increase in the mean of the intervals between adjacent QRS complexes or the instantaneous heart rate (HR) (RRI) (p=0.002; p=0.009, respectively), along with a significant reduction in HR (p=0.002; p=0.004, respectively). But, a significant reduction in systolic blood pressure (SBP) (p=0.037) and pulse pressure (PP) (p=0.017) was observed in NSSG, while a significant reduction in diastolic blood pressure (DBP) (p=0.008), mean arterial blood pressure (MAP) (p=0.008) and LF/HF ratio (p=0.041) was observed in NSBG. Conclusion Results of the study suggest that 15 min of both NSB and NSS might be effective in reducing HR and improving HRV. However, NSS is particularly effective in reducing SBP and PP, while NSB is particularly effective in reducing DBP and MAP along with improving sympathovagal balance in healthy volunteers.

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Machine Learning-Based Rapid Prediction of Sudden Cardiac Death (SCD) Using Precise Statistical Features of Heart Rate Variability for Single Lead ECG Signal

Green Computing and Predictive Analytics for Healthcare ◽

10.1201/9780429317224-4 ◽

2020 ◽

pp. 65-80

Author(s):

Prakash Banerjee

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Sudden Cardiac Death ◽

Cardiac Death ◽

Ecg Signal ◽

Statistical Features ◽

Rapid Prediction

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Exploring the Feasibility of Face Video Based Instantaneous Heart-Rate for Micro-Expression Spotting

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ◽

10.1109/cvprw.2018.00179 ◽

2018 ◽

Cited By ~ 1

Author(s):

Puneet Gupta ◽

Brojeshwar Bhowmick ◽

Arpan Pal

Keyword(s):

Heart Rate ◽

Instantaneous Heart Rate ◽

Micro Expression

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A machine learning approach to identifying objective biomarkers of anxiety and stress

10.1101/745315 ◽

2019 ◽

Cited By ~ 1

Author(s):

Arjun Ramakrishnan ◽

Adam Pardes ◽

William Lynch ◽

Christopher Molaro ◽

Michael Louis Platt

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Trait Anxiety ◽

Video Game ◽

Social Stress ◽

Internal State ◽

Self Report ◽

Support Vector ◽

High Trait ◽

High Trait Anxiety

AbstractAnxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state. Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety – a vulnerability foreshadowing development of anxiety and mood disorders – remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

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Trends in Heart-Rate Variability Signal Analysis

Frontiers in Digital Health ◽

10.3389/fdgth.2021.639444 ◽

2021 ◽

Vol 3 ◽

Author(s):

Syem Ishaque ◽

Naimul Khan ◽

Sri Krishnan

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Well Being ◽

Good Health ◽

Machine Learning Techniques ◽

Video Gaming ◽

Clinical Events ◽

Learning Techniques

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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