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

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...
Pulse rhythm01:30

Pulse rhythm

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 muscle...
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...
Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...

You might also read

Related Articles

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

Sort by
Same author

Physalin A Suppresses Human Oral Squamous Carcinoma Cell Migration and Invasion Through Inhibiting Grb2/Ras and MMP/uPA Signaling Pathways.

In vivo (Athens, Greece)·2026
Same author

Incorporating innovative B-cell targeting therapeutics into a combined-modality bridging approach followed by timely consolidative haploidentical transplantation to salvage a pediatric patient with relapsed/refractory diffuse large B-cell lymphoma.

Pediatric blood & cancer·2024
Same author

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Sensors (Basel, Switzerland)·2024
Same author

Emotion Recognition Based on Electroencephalogram Using Semi-supervised Generative Adversarial Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

Circulating clover-leaf cells presenting in acute-type adult T-cell leukemia-lymphoma.

EJHaem·2022
Same author

Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: May 20, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability.

Sung-Nien Yu1, Ming-Yuan Lee

  • 1Department of Electrical Engineering, National Chung Cheng University, Ming-Hsiung Township, Chia-Yi County, Taiwan. ieesny@ccu.edu.tw

Computers in Biology and Medicine
|July 20, 2012
PubMed
Summary

This study introduces a novel method for recognizing congestive heart failure (CHF) using bispectrum analysis of heart rate variability (HRV) and genetic algorithms (GA) for feature selection, achieving high accuracy.

More Related Videos

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

Related Experiment Videos

Last Updated: May 20, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Congestive heart failure (CHF) diagnosis relies on accurate analysis of physiological signals.
  • Heart rate variability (HRV) analysis is a key tool, but traditional methods may lack discrimination power.
  • Advanced signal processing techniques are needed to improve CHF detection.

Purpose of the Study:

  • To develop and evaluate a CHF recognition method incorporating bispectrum-derived HRV features.
  • To assess the efficacy of genetic algorithms (GA) for optimizing feature selection in CHF classification.
  • To enhance the diagnostic accuracy of CHF detection systems.

Main Methods:

  • Calculation of bispectrum-related features from HRV signals.
  • Integration of bispectrum features with traditional time-domain and frequency-domain HRV features.
  • Application of a support vector machine (SVM) classifier.
  • Utilization of a genetic algorithm (GA) for automated feature selection.

Main Results:

  • The proposed method, including bispectrum features, significantly improved classification accuracy compared to methods using only traditional HRV features.
  • The CHF recognition accuracy reached 96.38% without GA, outperforming existing literature.
  • Employing GA for feature selection further boosted accuracy to 98.79%, surpassing recent benchmarks.

Conclusions:

  • Bispectrum-related features are crucial for enhancing the discriminative capability in CHF classification.
  • Genetic algorithms effectively optimize feature selection, leading to superior performance in CHF recognition.
  • The proposed approach offers a promising, highly accurate tool for CHF detection.