  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.
