A Novel Heart Disease Classification Algorithm based on Fourier Transform and Persistent Homology

TL;DR

This paper introduces a new heart disease classification method using Fourier transform and persistent homology to analyze electrocardiograms, extracting topological features that improve disease differentiation.

Contribution

It combines Fourier transform and persistent homology to extract novel topological features from ECG data for improved heart disease classification.

Findings

Topological features effectively differentiate heart disease types.

The method shows promising classification accuracy.

Persistent homology captures ECG structural characteristics.

Abstract

Classification and prediction of heart disease is a significant problem to realize medical treatment and life protection. In this paper, persistent homology is involved to analyze electrocardiograms and a novel heart disease classification method is proposed. Each electrocardiogram becomes a point cloud by sliding windows and fast Fourier transform embedding. The obtained point cloud reveals periodicity and stability characteristics of electrocardiograms. By persistent homology, three topological features including normalized persistent entropy, maximum life of time and maximum life of Betty number are extracted. These topological features show the structural differences between different types of electrocardiograms and display encouraging potentiality in classification of heart disease.