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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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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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Decreased pulse rate01:14

Decreased pulse rate

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Bradycardia is a medical condition in which the heart rate is slower than normal. It occurs when the heart's natural pacemaker, the sinus node, generates slower electrical impulses than the standard rhythm. In adults, bradycardia is diagnosed when the pulse rate falls below 60 beats per minute, indicating a deviation from the normal heart rate range.
There are specific risk factors that can elevate the likelihood of developing bradycardia. Advanced age is a significant factor, with...
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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
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Updated: Aug 16, 2025

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
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Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm.

Angela An1,2, Mohammad Al-Fawa'reh2, James Jin Kang2,3

  • 1School of Information Technology, Deakin University, Burwood, VIC 3125, Australia.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

Machine learning using the damped least-squares algorithm (DLSA) improves telehealth data transmission efficiency and accuracy. This method reduces data volume and transmission frequency, enhancing wearable device battery life without sacrificing data integrity.

Keywords:
damped least-squares algorithm (DLSA)data accuracydata efficiencyhealthcareinference algorithmmachine learningneural networkstraining algorithm

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Area of Science:

  • Biomedical Engineering
  • Machine Learning Applications
  • Telehealth Technology

Background:

  • Telehealth systems face challenges in monitoring remote patients' vital signs, particularly with data volume from Internet of Things (IoT) wearable devices.
  • High data processing and transmission requirements of wearables deplete limited battery power, impacting continuous patient monitoring.
  • Maintaining data integrity and availability is crucial, yet increasing data accuracy often reduces transmission efficiency.

Purpose of the Study:

  • To demonstrate how machine learning (ML) can overcome the trade-off between accuracy and efficiency in telehealth data transmission.
  • To introduce and evaluate the damped least-squares algorithm (DLSA) for enhancing both data transmission accuracy and efficiency.
  • To improve wearable device battery life by reducing data volume and transmission frequency without compromising medical data quality.

Main Methods:

  • Implemented a machine learning approach utilizing the damped least-squares algorithm (DLSA).
  • Reduced data sampling frequency for transmission to conserve device power.
  • Tested the DLSA algorithm on a standard heart rate dataset to evaluate performance metrics.

Main Results:

  • The DLSA achieved an efficiency improvement of 3.33 times for reduced data size while maintaining high accuracy (95.6%).
  • Consistent high accuracies were observed across seven different sampling cases, demonstrating robust performance.
  • The proposed ML method significantly improved both efficiency and accuracy compared to existing methods.

Conclusions:

  • Machine learning, specifically the DLSA, effectively resolves the accuracy-efficiency trade-off in telehealth data transmission.
  • The DLSA enables reduced data transmission and improved battery life for wearable devices without compromising data integrity.
  • This approach offers a significant advancement for efficient and accurate remote patient monitoring in telehealth systems.