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.
Deep learning for ecg analysis: Benchmarks and insights from ptb-xl
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.
citing papers explorer
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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.
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Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.