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.
Cardiac arrhythmia detection using deep learning approach and time frequency representation of ecg signals
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ECG sampling frequency substantially and architecture-dependently impacts accuracy, sensitivity, and calibration of CNN and CNN-LSTM models for atrial fibrillation detection on resampled PTB-XL data.
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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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Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection
ECG sampling frequency substantially and architecture-dependently impacts accuracy, sensitivity, and calibration of CNN and CNN-LSTM models for atrial fibrillation detection on resampled PTB-XL data.