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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
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Correlation between ECG and Cardiac Cycle01:25

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Regulation of Heart Rates01:31

Regulation of Heart Rates

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The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

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Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart...
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Related Experiment Video

Updated: Oct 15, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Trends in Heart-Rate Variability Signal Analysis.

Syem Ishaque1, Naimul Khan1, Sri Krishnan1

  • 1Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON, Canada.

Frontiers in Digital Health
|October 29, 2021
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Summary

Heart rate variability (HRV) analysis reveals reduced HRV is linked to increased stress and illness. Advancements in machine learning can improve HRV detection during motion for better health insights.

Keywords:
drowsinessexerciseheart rate variabilitymachine learningmorbiditystresswireless sensors

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Area of Science:

  • Physiology
  • Biomedical Engineering
  • Data Science

Background:

  • Heart rate variability (HRV) quantifies beat-to-beat variations, reflecting Autonomic Nervous System (ANS) function.
  • The ANS regulates critical unconscious bodily functions, including cardiac activity, respiration, and blood pressure.
  • HRV analysis is a valuable tool for assessing physiological stress and overall health.

Purpose of the Study:

  • To review and analyze research on HRV in relation to morbidity, pain, drowsiness, stress, and exercise.
  • To identify research gaps and areas for improvement in HRV analysis methodologies.
  • To evaluate the efficacy of signal processing and machine learning in HRV studies.

Main Methods:

  • Systematic review of 25 articles focusing on Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals.
  • Analysis of signal processing and machine learning techniques applied to HRV data.
  • Assessment of HRV detection accuracy in ambulatory and motion conditions.

Main Results:

  • Reduced HRV is consistently associated with increased morbidity and stress levels.
  • High HRV generally indicates good health, though it can also signal clinical events like drowsiness.
  • HRV detection accuracy during motion (exercise, driving) varies significantly (59%-85%).

Conclusions:

  • HRV is a key indicator of physiological state, with reduced variability linked to negative health outcomes.
  • Improving HRV detection in motion, particularly during exercise and driving, holds potential for enhanced social well-being.
  • Further advancements in machine learning are crucial for refining HRV analysis and its clinical applications.