pith:Y6EOQ2DH
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
Nonlinear dimensionality reduction clusters ECG heartbeats from one person into normal and arrhythmic groups without labels or training.
arxiv:2506.16494 v3 · 2025-06-19 · cs.LG · eess.SP
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Claims
nonlinear dimensionality reduction (NLDR) algorithms, e.g., t-SNE and UMAP, can identify medically relevant features in ECG signals without pretraining or prior information. [...] applying NLDR to heartbeats of a single individual separates normal beats from arrhythmias into distinct clusters, identifiable in an unsupervised manner. Classification on 2D embeddings outperforms the original high-dimensional space, with a k-NN classifier discriminating individual recordings with >=80% accuracy and identifying arrhythmias with median accuracy >=98% and median F1-score >=85%.
That the distinct clusters formed in the 2D latent space correspond specifically to medically relevant arrhythmia categories rather than other sources of morphological variation or noise in the ECG signals.
Nonlinear dimensionality reduction on ECG signals enables unsupervised personalized arrhythmia detection with high accuracy on 2D embeddings using standard algorithms on the MIT-BIH database.
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| First computed | 2026-06-23T01:12:46.047523Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y6EOQ2DHUGASWEFVPE3PVQDTFL \
| jq -c '.canonical_record' \
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# expect: c788e86867a1812b10b57936fac0732ac272cfb41e142b6f991f4664f82cbcbc
Canonical record JSON
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