Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias

Abstract

Electrocardiograms (ECGs) provide direct, non-invasive measurements of heart activity and are well-established tools for detecting and monitoring cardiovascular disease. However, manual ECG analysis can be time-consuming and prone to errors. Machine learning has emerged as a promising approach for automated heartbeat recognition and classification, but substantial variations in ECG signals make it challenging to develop generalizable supervised models. ECG signals vary widely across individuals and leads, while datasets often follow different labeling standards and may be biased, greatly hindering supervised methods. Conventional unsupervised methods, such as principal component analysis, prioritize large (often obvious) variances and typically overlook subtle yet clinically relevant patterns. When labels are missing or variations are small, both approaches fail. Here, we show that nonlinear dimensionality reduction (NLDR) algorithms, namely t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), can address these challenges and identify medically relevant features in ECG signals without training or prior information. Using lead II and V1 signals from the MIT-BIH dataset, UMAP and t-SNE generate rich two-dimensional latent spaces with visually separable clusters. Applied to mixed populations of heartbeats, these clusters correspond to different individuals, while for single subjects they reveal distinct arrhythmia patterns. A simple classifier on these embeddings discriminates individual recordings with >= 90% accuracy and identifies arrhythmias in single patients with a median accuracy of 98.96% and median F1-score of 91.02%. The results show that NLDR holds much promise for cardiac monitoring, including the limiting cases of single-lead ECG and the current 12-lead standard of care, and for personalized health care beyond cardiology.