ECG Classification based on Wasserstein Scalar Curvature

TL;DR

This paper introduces a novel ECG classification method using Wasserstein scalar curvature, transforming ECG signals into Gaussian point clouds to extract pathological features through geometric analysis, demonstrating high accuracy and efficiency.

Contribution

It presents a new geometric approach for ECG classification based on Wasserstein scalar curvature, combining mathematical and medical insights for improved diagnosis.

Findings

High classification accuracy on large ECG datasets

Efficient algorithm with strong theoretical foundation

Effective differentiation between various heart diseases

Abstract

Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scalar curvature to comprehend the connection between heart disease and mathematical characteristics of ECG. The newly proposed method converts an ECG into a point cloud on the family of Gaussian distribution, where the pathological characteristics of ECG will be extracted by the Wasserstein geometric structure of the statistical manifold. Technically, this paper defines the histogram dispersion of Wasserstein scalar curvature, which can accurately describe the divergence between different heart diseases. By combining medical experience with mathematical ideas from geometry and data science, this paper provides a feasible algorithm for the new method, and the theoretical analysis of the algorithm is carried out. Digital experiments on the classical database with large samples show the new algorithm's accuracy and efficiency when dealing with the classification of heart disease.