Papers ECG Classification
“ECG Classification” 태그가 달린 논문 153편 · 필터 해제
ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation
Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders their clinical deployment. Trust in medical AI requires not just high…
ECG ClassificationHow Much Temporal Modeling is Enough? A Systematic Study of Hybrid CNN-RNN Architectures for Multi-Label ECG Classification
Accurate multi-label classification of electrocardiogram (ECG) signals remains challenging due to the coexistence of multiple cardiac conditions, pronounced class imbalance, and long-range temporal dependencies in multi-…
Multi-Label ClassificationECG ClassificationEnhancing Imbalanced Electrocardiogram Classification: A Novel Approach Integrating Data Augmentation through Wavelet Transform and Interclass Fusion
Imbalanced electrocardiogram (ECG) data hampers the efficacy and resilience of algorithms in the automated processing and interpretation of cardiovascular diagnostic information, which in turn impedes deep learning-based…
ECG ClassificationData AugmentationBEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Classification
Although deep learning has advanced automated electrocardiogram (ECG) diagnosis, prevalent supervised methods typically treat recordings as undifferentiated one-dimensional (1D) signals or two-dimensional (2D) images. Th…
ECG ClassificationTransferring Clinical Knowledge into ECGs Representation
Deep learning models have shown high accuracy in classifying electrocardiograms (ECGs), but their black box nature hinders clinical adoption due to a lack of trust and interpretability. To address this, we propose a nove…
Clinical KnowledgeECG ClassificationSimulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well-annotated ECG corpora are scarce due to…
Clinical KnowledgeECG ClassificationFederated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices
This study presents a federated learning (FL) framework for privacy-preserving electrocardiogram (ECG) classification in Internet of Things (IoT) healthcare environments. By transforming 1D ECG signals into 2D Gramian An…
Federated LearningECG ClassificationEnhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters
Continuous electrocardiogram (ECG) monitoring via wearable devices is vital for early cardiovascular disease detection. However, deploying deep learning models on resource-constrained microcontrollers faces reliability c…
Unsupervised Anomaly DetectionNeural Architecture SearchECG ClassificationFrom Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
In eXplainable Artificial Intelligence (XAI), instance-based explanations for time series have gained increasing attention due to their potential for actionable and interpretable insights in domains such as healthcare. A…
Multi-class ClassificationECG ClassificationEcoScaleNet: A Lightweight Multi Kernel Network for Long Sequence 12 lead ECG Classification
Accurate interpretation of 12 lead electrocardiograms (ECGs) is critical for early detection of cardiac abnormalities, yet manual reading is error prone and existing CNN based classifiers struggle to choose receptive fie…
ECG ClassificationComparative Analysis of Data Augmentation for Clinical ECG Classification with STAR
Clinical 12-lead ECG classification remains difficult because of diverse recording conditions, overlapping pathologies, and pronounced label imbalance hinder generalization, while unconstrained augmentations risk distort…
ECG ClassificationData AugmentationOne Dimensional CNN ECG Mamba for Multilabel Abnormality Classification in 12 Lead ECG
Accurate detection of cardiac abnormalities from electrocardiogram recordings is regarded as essential for clinical diagnostics and decision support. Traditional deep learning models such as residual networks and transfo…
ECG ClassificationFoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis
Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analysis is central to detecting cardiac abno…
ECG ClassificationCommunication-Efficient Federated Learning with Adaptive Number of Participants
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framework to address these concerns by enabling…
Federated LearningECG ClassificationPrototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
Prototype-based neural networks offer interpretable predictions by comparing inputs to learned, representative signal patterns anchored in training data. While such models have shown promise in the classification of phys…
ECG ClassificationRethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification
This study proposes a novel perspective on multimodal deep learning for biomedical signal classification, systematically analyzing how complementary feature domains impact model performance. While fusing multiple domains…
Multimodal Deep LearningBayesian InferenceECG ClassificationNeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignment
We present a novel and interpretable framework for electrocardiogram (ECG)-based disease detection that combines hyperdimensional computing (HDC) with learnable neural encoding. Unlike conventional HDC approaches that re…
Representation LearningECG ClassificationCardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture
Accurate ECG interpretation is vital, yet complex cardiac data and "black-box" AI models limit clinical utility. Inspired by Transformer architectures' success in NLP for understanding sequential data, we frame ECG as th…
DiagnosticECG ClassificationCardioformer: Advancing AI in ECG Analysis with Multi-Granularity Patching and ResNet
Electrocardiogram (ECG) classification is crucial for automated cardiac disease diagnosis, yet existing methods often struggle to capture local morphological details and long-range temporal dependencies simultaneously. T…
ECG ClassificationMitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial Training
Federated Learning (FL) has become an important research area in recent years, particularly when dealing with sensitive data such as healthcare information. Since healthcare data contains critical and personal informatio…
ECG ClassificationElectrocardiography (ECG)Federated Learning