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Atrial Fibrillation Detection
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals
Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals
A Review on Multisensor Data Fusion for Wearable Health Monitoring
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones
Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals
SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling
Compressor-Based Classification for Atrial Fibrillation Detection
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Sparse learned kernels for interpretable and efficient medical time series processing
Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG
Atrial Fibrillation Detection Using RR-Intervals for Application in Photoplethysmographs
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices
Non-contact Atrial Fibrillation Detection from Face Videos by Learning Systolic Peaks
Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data
Construe: a software solution for the explanation-based interpretation of time series
HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals
Atrial Fibrillation Detection Using Deep Features and Convolutional Networks
ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.