Papers ECG Classification
“ECG Classification” 태그가 달린 논문 153편 · 필터 해제
Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient populations. Test-tim…
Test-time AdaptationECG ClassificationHybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training. Federated learning addresses this practical pr…
Federated LearningECG ClassificationCardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation
Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, …
ECG ClassificationLSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
Deploying deep learning models for automated electrocardiogram classification on resource-constrained wearable devices remains challenging due to high computational costs. To address this, we propose LSTrans, a lightweig…
Knowledge DistillationECG ClassificationDual Attention Heads for Personalized Federated Learning in ECG Classification
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterogeneity of electrocardiogram (ECG) data across healthcare providers pre…
Personalized Federated LearningECG ClassificationUsing Explainability as a Training-Time Reliability Signal for Efficient ECG Classification
Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment. This challenge is part…
ECG ClassificationBoosting ECG Classification Performance by Pre-training with Synthesized Data
Deep Neural Networks (DNNs) typically require extensive datasets for effective training. In the medical domain, acquiring large-scale data is often challenging due to privacy concerns and the rarity of certain diseases. …
ECG ClassificationSafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi-supervised learning (SSL) an attractive strategy for reducing annotation costs. In clinical settings, however, unlabeled …
ECG ClassificationHeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection
While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization gap. This paper presents HeartBeatAI, a deep learning framework comb…
Domain GeneralizationArrhythmia DetectionECG ClassificationHexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities
The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. However, detecting concurrent conditions remains a challenge for tradition…
Multi-Label ClassificationECG ClassificationReasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification
Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, conduction properties, waveform morphology, and overall diagnostic impre…
ECG ClassificationECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification
Electrocardiogram (ECG) arrhythmia classification remains challenging due to signal variability, noise, limited labeled data, and the difficulty in achieving both accuracy and efficiency in models. While self-supervised …
Self-Supervised LearningComputational EfficiencyRepresentation LearningECG ClassificationEfficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware
Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential for automating ECG classification, many existing models rely on lar…
Computational EfficiencyECG ClassificationECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset
Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compares three traditional machine learning algorithms (Decision Tree Classif…
ECG ClassificationData AugmentationFrom Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student
Foundation models have recently improved electrocardiogram (ECG) representation learning, but their deployment can be limited by computational cost and latency constraints. In this work, we fine-tune ECGFounder as a high…
Representation LearningKnowledge DistillationECG ClassificationDemographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities in diagnostic performance. These limitati…
Self-Supervised Anomaly DetectionRepresentation LearningECG ClassificationElectrocardiogram Classification with Transformers Using Koopman and Wavelet Features
Electrocardiogram (ECG) analysis is vital for detecting cardiac abnormalities, yet robust automated classification is challenging due to the complexity and variability of physiological signals. In this work, we investiga…
Binary ClassificationECG ClassificationECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE
Automated electrocardiogram (ECG) classification is essential for early detection of cardiovascular diseases. While recent approaches have increasingly relied on deep neural networks with complex architectures, we demons…
ECG ClassificationCAMEL: An ECG Language Model for Forecasting Cardiac Events
Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a promising framework for ECG classification…
ECG ClassificationContrastive Learning for Multi Label ECG Classification with Jaccard Score Based Sigmoid Loss
Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high accuracy on VQA tasks like USMLE MM, their performance on ECG based tasks…
Contrastive LearningECG Classification