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.
Rajoub, ”Machine learning in biomedical signal processing with ECG applications,” inBiomedical Signal Processing and Artificial Intelli- gence in Healthcare, W
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Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
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.