An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions
Author:
Publisher
Elsevier BV
Subject
Health Informatics,Computer Science Applications
Reference28 articles.
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1. Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023);WIREs Data Mining and Knowledge Discovery;2024-02-04
2. Optimal fused feature selection with ensemble learning foratrial fibrillation detection using ECG with enhanced average and subtraction-based optimizer;Intelligent Decision Technologies;2023-11-20
3. A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism;Computer Methods and Programs in Biomedicine;2023-06
4. ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System;Bioengineering;2023-01-13
5. Automated diagnosis of atrial fibrillation using ECG component-aware transformer;Computers in Biology and Medicine;2022-11
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