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.
Clinical IoT in Practice: A Novel Design and Implementation of a Multi-functional Digital Stethoscope for Remote Health Monitoring
2 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Introduces a new dataset of manikin-recorded separate and mixed cardiopulmonary sounds with normal and pathological variants captured at multiple anatomical locations using a digital stethoscope.
citing papers explorer
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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.
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Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital Stethoscope
Introduces a new dataset of manikin-recorded separate and mixed cardiopulmonary sounds with normal and pathological variants captured at multiple anatomical locations using a digital stethoscope.