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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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Disturbances in Heart Rhythm01:29

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
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Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

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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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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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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.
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Effects of Missing Data on Heart Rate Variability Metrics.

Diego Cajal1,2, David Hernando1,2, Jesús Lázaro1,2

  • 1Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary

Data loss in wearable heart rate variability (HRV) analysis impacts metrics differently. Correction without gap filling suits burst data loss for time-domain HRV, while gap filling benefits scattered data loss and frequency-domain HRV.

Keywords:
ANSApple WatchHRVPoincaré plots

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Area of Science:

  • Cardiovascular physiology
  • Biomedical engineering
  • Signal processing

Background:

  • Heart rate variability (HRV) analysis is crucial in clinical settings.
  • Wearable devices present challenges for HRV due to poor signal quality and motion artifacts, causing data loss.
  • Understanding data loss impact on HRV metrics is vital for reliable health monitoring.

Purpose of the Study:

  • To investigate the effects of data loss on time-domain, frequency-domain, and Poincaré plot HRV metrics.
  • To propose and evaluate a novel gap-filling method for mitigating data loss in HRV analysis.
  • To determine optimal data correction strategies based on data loss patterns and HRV metrics.

Main Methods:

  • Simulated and real-world datasets with scattered and burst missing beats were analyzed.
  • Proposed gap-filling method was compared against existing approaches.
  • Photoplethysmography (PPG) data from Apple Watch during rest and stress protocols were utilized.

Main Results:

  • Correction without gap filling is optimal for SDNN, RMSSD, and Poincaré plots with predominant burst missing beats.
  • Gap-filling methods are superior for scattered missing beats and all frequency-domain HRV metrics.
  • Significant differences (p<0.05) were observed based on data loss type and correction method.

Conclusions:

  • The choice of data correction method for HRV analysis depends on the nature of data loss (scattered vs. burst) and the specific HRV metrics of interest.
  • Findings guide the development of robust HRV applications for wearable health monitoring.
  • Optimal strategies balance missing data tolerance with desired metric accuracy.