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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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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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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).
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A Predictive Analysis of Heart Rates Using Machine Learning Techniques.

Matthew Oyeleye1, Tianhua Chen1, Sofya Titarenko1

  • 1Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.

International Journal of Environmental Research and Public Health
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

Predicting heart rate using accelerometer data is crucial for early heart disease detection. This study found autoregressive integrated moving average (ARIMA) and linear regression models effective for heart rate prediction from wearable sensors.

Keywords:
accelerometerdata analyticsheart ratemachine learningtime series

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Area of Science:

  • Biomedical Engineering
  • Data Science in Healthcare
  • Cardiovascular Health Monitoring

Background:

  • Heart disease is a leading global cause of mortality.
  • Early detection of heart rate irregularities is critical for managing cardiovascular health.
  • Advanced technologies like IoT, wearables, and AI are transforming healthcare data analysis.

Purpose of the Study:

  • To analyze the effectiveness of data analytics and machine learning for monitoring and predicting heart rates.
  • To evaluate various data-driven models using accelerometer-generated data for heart rate prediction.
  • To assess the accuracy of future heart rate predictions from time-series data.

Main Methods:

  • Explored autoregressive integrated moving average (ARIMA), linear regression, support vector regression (SVR), k-nearest neighbor (KNN), decision tree, random forest, and long short-term memory (LSTM) models.
  • Analyzed univariant heart rate time-series data from accelerometers of healthy individuals.
  • Evaluated model performance under different prediction durations using a recent dataset.

Main Results:

  • Autoregressive integrated moving average (ARIMA) with walk-forward validation and linear regression demonstrated effectiveness in predicting heart rate across all durations.
  • Other models, including random forest and LSTM, showed effectiveness for predictions longer than 1 minute.
  • Experimental results confirm the utility of these data analytics techniques for accurate future heart rate prediction.

Conclusions:

  • Data analytics and machine learning models, particularly ARIMA and linear regression, can accurately predict future heart rates using accelerometer data.
  • Wearable sensor data combined with advanced algorithms offers a promising approach for proactive cardiovascular health monitoring.
  • This study highlights the potential of technology in early disease detection and personalized healthcare.