Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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,...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Regulation of Heart Rates01:31

Regulation of Heart Rates

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...
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cellular Senescence in Idiopathic Pulmonary Fibrosis: Molecular Mechanisms, Pathogenic Networks, and Emerging Therapeutic Targets.

Diseases (Basel, Switzerland)·2026
Same author

Use of Machine Learning in Predicting the Risk of Cirrhosis in Autoimmune Hepatitis Based on Clinical and Immunological Indicators.

Diagnostics (Basel, Switzerland)·2026
Same author

Multidimensional Visualization and AI-Driven Prediction Using Clinical and Biochemical Biomarkers in Premature Cardiovascular Aging.

Biomedicines·2025
Same author

Multimodal Computational Approach for Forecasting Cardiovascular Aging Based on Immune and Clinical-Biochemical Parameters.

Diagnostics (Basel, Switzerland)·2025
Same author

Immunological Markers of Cardiovascular Pathology in Older Patients.

Biomedicines·2025
Same author

A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning.

Diagnostics (Basel, Switzerland)·2025

Related Experiment Videos

Predicting the Risk of Cardiovascular Diseases in the Elderly Based on Clinical Data and Heart Rate Variability Using

Kuat Abzaliyev1, Akbota Bugibayeva2, Symbat Abzaliyeva1

  • 1Department of Big Data and Artificial Intelligence, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.

Journal of Clinical Medicine
|July 15, 2026
PubMed
Summary

Reduced heart rate variability (HRV) in older adults indicates a higher risk of cardiovascular disease (CVD). Photoplethysmography-derived HRV combined with machine learning improves CVD risk prediction.

Keywords:
cardiovascular diseaseheart rate variabilitymachine learningphotoplethysmographypredicting

Related Experiment Videos

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Gerontology

Background:

  • Cardiovascular diseases (CVDs) are a leading cause of death in the elderly.
  • Autonomic nervous system (ANS) activity is closely linked to cardiovascular mortality.
  • Heart rate variability (HRV), derived from photoplethysmography (PPG), is a key indicator of ANS function and CVD risk.

Purpose of the Study:

  • To assess the predictive capability of HRV indicators for CVD risk in individuals aged 65 and older.
  • To explore the utility of machine learning algorithms in analyzing PPG-derived HRV for CVD risk stratification.

Main Methods:

  • The study included 100 individuals aged 65+, divided into CVD risk and no-risk groups.
  • Photoplethysmography (PPG) was used to collect signals for time-domain and spectral HRV analysis.
  • Interpretable machine learning models (logistic regression, random forest) were developed and evaluated using cross-validation.

Main Results:

  • Elderly patients with CVD exhibited significantly reduced HRV (SDNN, pNN50, TINN, HRV) and altered spectral components (VLF, LF, HF).
  • Increased VLF/HF and LF/HF ratios indicated sympathetic nervous system dominance.
  • Machine learning models, particularly random forest, incorporating HRV features demonstrated high accuracy (ROC-AUC 0.9988) in predicting CVD risk.

Conclusions:

  • A strong association exists between CVD risk and autonomic nervous system dysfunction, evidenced by decreased HRV.
  • PPG-derived HRV, analyzed with machine learning, offers enhanced diagnostic and prognostic value for CVD risk stratification in the elderly.
  • Reduced HRV in older adults serves as a potential marker for developing advanced CVD diagnostic and risk management strategies.