Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus, Enrique; Miramontes, Pedro; Martínez-García, Mireya

doi:10.3390/e26010067

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…Several approaches to computer-aided ECG rhythm classification have been performed, including neural networks [5,15,17,21,25,39,46,48,51,56,61,62,[66][67][68], wavelet transformation and independent component analysis [31,65], using higher-order statistics of wavelet-packet decomposition coefficients as features [32], and support vector machines using projected and dynamic ECG features [9]. An overview of TDA applied to cardiovascular signals has recently been performed [23]. In the field of computer-aided ECG analysis, TDA has been used to construct metrics of heart rate variability [11,20].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram arrhythmia detection with novel signal processing and persistent homology-derived predictors

Dlugas

2024

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Many approaches to computer-aided electrocardiogram (ECG) arrhythmia detection have been performed, several of which combine persistent homology and machine learning. We present a novel ECG signal processing pipeline and method of constructing predictor variables for use in statistical models. Specifically, we introduce an isoelectric baseline to yield non-trivial topological features corresponding to the P, Q, S, and T-waves (if they exist) and utilize the N-most persistent 1-dimensional homological features and their corresponding area-minimal cycle representatives to construct predictor variables derived from the persistent homology of the ECG signal for some choice of N. The binary classification of (1) Atrial Fibrillation vs. Non-Atrial Fibrillation, (2) Arrhythmia vs. Normal Sinus Rhythm, and (3) Arrhythmias with Morphological Changes vs. Sinus Rhythm with Bradycardia and Tachycardia Treated as Non-Arrhythmia was performed using Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes, Random Forest, Gradient Boosted Decision Tree, K-Nearest Neighbors, and Support Vector Machine with a linear, radial, and polynomial kernel Models with stratified 5-fold cross validation. The Gradient Boosted Decision Tree Model attained the best results with a mean F1-score and mean Accuracy of ( 0.967 , 0.946 ), ( 0.839 , 0.946 ), and ( 0.943 , 0.921 ) across the five folds for binary classifications of (1), (2), and (3), respectively.

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