Related Experiment Video
Updated: Apr 3, 2026
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
Automated multi-class ECG arrhythmia detection using VMD and multi-task optimization
Y Murali Krishna1, K Padma Vasavi2, M Krishna Chaitanya3
1ECE, QIS College of Engineering and Technology, Vengamukalapalem, Ongole, 523272, Andhra Pradesh, India.
Insights
This study introduces an advanced framework for classifying cardiac arrhythmias from ECG signals. The optimized feature set significantly improved detection accuracy for conditions like Atrial Fibrillation.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Accurate Electrocardiogram (ECG) classification is crucial for diagnosing heart rhythm disorders.
- Existing methods may face challenges in distinguishing between various complex arrhythmias.
- The need for robust and efficient ECG analysis frameworks remains high in clinical practice.
Purpose of the Study:
- To develop and evaluate a novel multi-class ECG classification framework.
- To identify four key cardiac conditions: Atrial Fibrillation (AF), Ventricular Fibrillation (VF), Normal Rhythm (NR), and Ventricular Tachycardia (VT).
- To enhance feature discriminability and reduce computational load through optimized feature selection.
Main Methods:
- ECG signals were processed using Variational Mode Decomposition (VMD).
- Higher-order statistics and entropy-based features were extracted from decomposed modes.
- Multi-task Particle Swarm Optimization (MT-PSO) was utilized for feature selection and reduction.
- Several machine learning models, including LightGBM, HistGradientBoost, XGBoost, and ExtraTrees, were evaluated.
Main Results:
- The optimized feature set significantly improved classification performance across all evaluated models.
- LightGBM achieved the highest accuracy (0.993), followed closely by HistGradientBoost (0.991), XGBoost (0.990), and ExtraTrees (0.990).
- Execution time was reduced for several models post-optimization, indicating increased efficiency.
- Confusion matrix and ROC analyses confirmed reliable detection of all four cardiac classes.
Conclusions:
- The proposed VMD-based feature extraction and MT-PSO optimization framework offers a highly effective approach for multi-class ECG classification.
- The framework demonstrates competitive or superior performance compared to existing methods for detecting cardiac arrhythmias.
- This approach holds promise for improving the accuracy and efficiency of automated cardiac diagnosis.
Abstract:
Electrocardiogram (ECG) classification is essential for accurately detecting and tracking heart rhythm disorders. This study proposes a multi-class ECG classification framework for identifying cardiac arrhythmias like Atrial Fibrillation (AF), Ventricular Fibrillation (VF), Normal Rhythm (NR), and Ventricular Tachycardia (VT). The ECG signals were decomposed using Variational Mode Decomposition (VMD), and higher-order statistics as well as entropy-based features were extracted from each mode. Multi-task Particle Swarm Optimization (MT-PSO) was employed to reduce redundant features and enhance the discriminative capability of the dataset. Multiple machine-learning models were evaluated, and optimized feature set led to clear performance improvements. The best results were obtained using LightGBM (ACC 0.993), HistGradientBoost (0.991), XGBoost (0.990), and ExtraTrees (0.990). Execution time also decreased for several models after optimization. Confusion-matrix and ROC analyses confirmed reliable detection across all four cardiac classes, and comparison with reported works shows that the proposed framework offers competitive or improved performance for ECG classification.
Related Concept Videos
01:25Correlation between ECG and 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...
01:25ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
01:24ECG Interpretation of Rhythms
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
01:30Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
01:28Dysrhythmias II: Classification of Tachyarrhythmias