Key result
UMAP-KNN outperforms logistic regression in classifying cardiac rhythms, particularly for imbalanced classes like AF.
Why the study?
The study aimed to analyze and compare different dimensionality reduction and classification methods for ECG data to classify five cardiac rhythms including arrhythmias.
UMAP combined with K-Nearest Neighbors provides superior performance for classifying cardiac rhythms from ECG data compared to PCA and logistic regression.
May advance automated ECG rhythm classification tools; leaves open prospective clinical validation before adoption.
This study aims to analyze electrocardiogram (ECG) data for the classification of five cardiac rhythms: sinus bradycardia (SB), sinus rhythm (SR), atrial fibrillation (AFIB), supraventricular tachycardia (SVT), and sinus tachycardia (ST). While SR is considered normal, the other four represent types of cardiac arrhythmias. A range of methods is utilized, including the supervised learning technique K-Nearest Neighbors (KNNs), combined with dimensionality reduction approaches such as Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), a modern method based in Riemannian topology. Additionally, logistic regression was applied using both maximum likelihood and Bayesian methods, with two distinct prior distributions: an informative normal prior and a non-informative Jeffreys prior. Performance was assessed using evaluation metrics such as positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, accuracy, and F1-score. Ultimately, the UMAP-KNN method demonstrated the best overall performance.
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Tchoffo et al. (2025) studied Cardiac arrhythmias (n=10,646). UMAP-KNN (Uniform Manifold Approximation and Projection with K-Nearest Neighbors) vs. PCA-KNN and Logistic Regression (Maximum Likelihood and Bayesian) was evaluated on Classification performance (F1-score, accuracy, sensitivity, specificity, PPV, NPV). The UMAP-KNN model demonstrated the most balanced and robust performance across five cardiac rhythms, outperforming logistic regression models particularly in imbalanced classes such as sinus rhythm, atrial fibrillation, and sinus tachycardia.
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