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Atrial Fibrillation Detection

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Showing 1–25 of 43 papers

TitleStatusHype
DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals—0
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals—0
Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals—0
A Review on Multisensor Data Fusion for Wearable Health Monitoring—0
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation—0
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones—0
Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System SignalsCode0
SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG SignalsCode1
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals—0
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG—0
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals—0
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019—0
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation DetectionCode1
PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling—0
Compressor-Based Classification for Atrial Fibrillation Detection—0
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG SignalCode0
Sparse learned kernels for interpretable and efficient medical time series processingCode1
Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG—0
Atrial Fibrillation Detection Using RR-Intervals for Application in Photoplethysmographs—0
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia AlarmsCode1
Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural NetworksCode1
Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learningCode1
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks—0
Investigating Deep Learning Benchmarks for Electrocardiography Signal ProcessingCode2
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge DevicesCode1
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