Related Experiment Video

Updated: Nov 5, 2025

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
06:57

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Published on: September 22, 2023

1.2K

Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN

V Jahmunah1, E Y K Ng1, Tan Ru San2

  • 1Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.

Computers in Biology and Medicine
|May 15, 2021
PubMed

Insights

This study introduces an automated system using GaborCNN and CNN models for classifying electrocardiogram (ECG) signals. The GaborCNN model achieved over 98.5% accuracy in detecting coronary artery disease (CAD), myocardial infarction (MI), and congestive heart failure (CHF).

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of mortality, with coronary artery disease (CAD) being the most prevalent.
  • Early detection of CAD and its complications, such as myocardial infarction (MI) and congestive heart failure (CHF), is critical for effective management.
  • Electrocardiogram (ECG) signals are a primary non-invasive tool for screening and diagnosing cardiac conditions.

Purpose of the Study:

  • To develop and evaluate an automated system for classifying ECG signals into four categories: normal, CAD, MI, and CHF.
  • To compare the performance of a novel GaborCNN model against a standard Convolutional Neural Network (CNN) for ECG signal analysis.
  • To establish a computationally efficient and accurate AI model for aiding clinicians in CVD screening.

Main Methods:

  • Development of an automated system utilizing both CNN and a unique GaborCNN architecture.
  • Implementation of weight balancing techniques to address dataset imbalance.
  • Training and validation of the models on ECG signal datasets for multi-class classification.

Main Results:

  • Both CNN and GaborCNN models achieved high classification accuracies exceeding 98.5% for the four-class ECG categorization.
  • The GaborCNN model demonstrated superior performance with reduced computational complexity compared to the CNN model.
  • This represents the first application of the GaborCNN model for automated classification of normal, CAD, MI, and CHF from ECG signals.

Conclusions:

  • The developed automated system, particularly the GaborCNN model, offers a highly accurate and efficient method for ECG-based cardiovascular disease screening.
  • The GaborCNN model presents a promising alternative to traditional CNNs for complex signal processing tasks in cardiology.
  • The system has the potential to support clinical decision-making and improve patient outcomes in cardiovascular healthcare.

Keywords:
Cardiovascular diseaseConvolutional neural networkDeep learningGabor convolutional neural networkGabor filterMulti-class classificationTen-fold validation

More Related Videos

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.5K
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

622

Related Concept Videos

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...
1.0K
Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
4.4K
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...
10.0K
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
62