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Basics of Multivariate Analysis in Neuroimaging Data

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Clinically interpretable multiclass neural network for discriminating cardiac diseases.

Agnese Sbrollini¹, Chiara Leoni¹, Micaela Morettini¹

¹Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.

|January 21, 2025

Summary

This study introduces an interpretable deep-learning tool for multiclass cardiac disease classification using electrocardiograms. The novel approach accurately distinguishes between various heart conditions, aiding clinical diagnosis.

Keywords:

Cardiac rhythm Deep learning Electrocardiography Multiclass neural network Repeated structuring & learning procedure Vectorcardiography

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Area of Science:

Cardiology
Artificial Intelligence
Machine Learning

Background:

Current deep learning in cardiology often uses simplified binary classification.
Real-world clinical scenarios require multiclass classification to differentiate among various cardiac diseases.
This study addresses the need for advanced deep learning tools in cardiology.

Purpose of the Study:

To develop a novel, interpretable deep-learning tool for multiclass classification of cardiac diseases.
To discriminate among several different cardiac conditions using electrocardiogram (ECG) data.
To enhance diagnostic capabilities in cardiology through advanced AI.

Main Methods:

Utilized the Physionet database from the "China Physiological Signal Challenge in 2018".
Developed a multiclass neural network using the Advanced Repeated Structuring & Learning Procedure (AdvRS&LP).
Processed 6877 12-lead ECGs, extracting 252 features for classification into eight categories.

Main Results:

Achieved classification performance ranging from 89.88% to 90.10% on the learning dataset.
Demonstrated performance from 69.15% to 91.14% on the testing dataset.
Results are considered strong given the complexity of the multiclass classification task.

Conclusions:

The proposed AdvRS&LP-based multiclass neural network shows promise for clinical application.
The tool effectively discriminates between multiple cardiac diseases.
Ensures clinical interpretability alongside high diagnostic performance.