Integrating Distance Correlation and Adaptive Weighting with RBF Kernel Transformations: A Novel Feature Selection

Monica Fira¹, Lucian Fira²

¹Institute of Computer Science, Romanian Academy, Iasi Branch, 700481 Iasi, Romania.

Bioengineering (Basel, Switzerland)

|May 4, 2026

Summary

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Correlation between ECG and Cardiac Cycle

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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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Arrhythmia Classification with Single-Channel Features Extracted from "A Large-Scale 12-Lead ECG Database for Arrhythmia Study".

Sensors (Basel, Switzerland)·2025

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This study presents a novel feature selection method for enhanced cardiac arrhythmia detection. It effectively captures complex data relationships, outperforming traditional techniques for improved medical diagnosis.

Area of Science:

Biomedical Informatics
Machine Learning
Cardiology

Background:

Accurate feature selection is vital for machine learning in medical diagnosis.
Conventional methods struggle with complex non-linear relationships in biomedical data.

Purpose of the Study:

To introduce an advanced feature selection approach integrating distance correlation and adaptive weighting for enhanced cardiac arrhythmia detection.
To improve the accuracy of machine learning models in classifying cardiac arrhythmias by addressing limitations of existing feature selection techniques.

Main Methods:

Proposed a novel feature selection method ranking features by distance correlation.
Applied inverse penalty weighting to manage feature correlations and RBF kernel transformation with LASSO refinement.

Keywords:

LASSO regularization adaptive weighting classification distance correlation feature selection kernel methods

Evaluated fifteen feature selection techniques on an electrocardiographic database using 4-fold cross-validation and a neural network classifier.

Main Results:

The proposed method significantly outperformed all alternative techniques, including conventional approaches.
Demonstrated superior performance in capturing non-linear dependencies and mitigating multicollinearity and overfitting.
Successfully leveraged synergistic kernel-based interaction modeling with sparse selection for robust feature identification.

Conclusions:

The combined approach of statistical dependence measures, adaptive regularization, and non-linear transformations offers a robust framework for feature selection.
This method enhances cardiac arrhythmia classification and has potential applications in broader medical informatics.
The study highlights the importance of advanced feature selection for accurate medical diagnosis using machine learning.

Related Experiment Video

Integrating Distance Correlation and Adaptive Weighting with RBF Kernel Transformations: A Novel Feature Selection...