Papers ECG Classification
“ECG Classification” 태그가 달린 논문 153편 · 필터 해제
xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic tools. Electrocardiograms (ECGs) are indispensable in diagnosing variou…
ClassificationDiagnosticECG ClassificationMulti-Label Classification+1ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency ma…
Contrastive LearningDeep LearningDiagnosticECG Classification+3Integrating Notch Filtering and Statistical Methods for Improved Cardiac Diagnostics Using MATLAB
A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially powerline interference at 50 Hz or 60 Hz. This interference overlaps with vital ECG signal features, affecting the accuracy o…
Arrhythmia DetectionClassificationECG ClassificationA Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility
The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, many studies fail to follow standardization…
ClassificationECG ClassificationConvolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency Approach for ECG Classification
Machine learning has revolutionized biomedical signal analysis, particularly in electrocardiogram (ECG) classification. While convolutional neural networks (CNNs) excel at automatic feature extraction, the optimal integr…
Arrhythmia DetectionClassificationdomain classificationECG ClassificationDCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits
DCentNet is a novel decentralized multistage signal classification approach designed for biomedical data from IoT wearable sensors, integrating early exit points (EEP) to enhance energy efficiency and processing speed. U…
ECG ClassificationSensitivityA General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm: Hardware Details and Real-life Applications
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SN…
ECG ClassificationGAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention
Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GA…
ECG ClassificationTime SeriesTime Series AnalysisAmpliNetECG12: A lightweight SoftMax-based relativistic amplitude amplification architecture for 12 lead ECG classification
The urgent need to promptly detect cardiac disorders from 12-lead Electrocardiograms using limited computations is motivated by the heart's fast and complex electrical activity and restricted computational power of porta…
ECG ClassificationLearning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
Electrocardiogram (ECG) captures the heart's electrical signals, offering valuable information for diagnosing cardiac conditions. However, the scarcity of labeled data makes it challenging to fully leverage supervised le…
ECG ClassificationElectrocardiography (ECG)Representation LearningSelf-Supervised Learning+1Heartbeat classification using various machine learning models: A comparative study
Cardiac arrhythmias, known as irregular heartbeats, pose a notable health threat that necessitates prompt diagnosis, as untreated arrhythmias can lead to severe heart complications. Among the various methods for arrhythm…
Arrhythmia DetectionECG ClassificationECG Denoisingfeature selection+2VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation
An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digitized signals or printed images. Despite the emergence of numerous deep …
ECG ClassificationKnowledge DistillationPrognosisNon-linear Analysis Based ECG Classification of Cardiovascular Disorders
Multi-channel ECG-based cardiac disorders detection has an impact on cardiac care and treatment. Limitations of existing methods included variation in ECG waveforms due to the location of electrodes, high non-linearity i…
ECG ClassificationOpen-World Electrocardiogram Classification via Domain Knowledge-Driven Contrastive Learning
Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for assisting disease diagnosis and has received much attention in research. The success of existing classification models relies o…
ClassificationClinical KnowledgeContrastive LearningDiagnostic+1ECGMamba: Towards Efficient ECG Classification with BiSSM
Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit ine…
ClassificationDiagnosticECG ClassificationMambaA Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification
Electrocardiograms (ECG), which record the electrophysiological activity of the heart, have become a crucial tool for diagnosing these diseases. In recent years, the application of deep learning techniques has significan…
ECG ClassificationRhythmSparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Heart disease is one of the leading causes of death worldwide. Given its high risk and often asymptomatic nature, real-time continuous monitoring is essential. Unlike traditional artificial neural networks (ANNs), spikin…
ECG ClassificationEdge-computingHeartbeat ClassificationFederated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data
This research paper explores ways to apply Federated Learning (FL) and Differential Privacy (DP) techniques to population-scale Electrocardiogram (ECG) data. The study learns a multi-label ECG classification model using …
ECG ClassificationFederated LearningBaseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study proposes Derived Peak (DP) encoding, a non-par…
ECG ClassificationEmpirical investigation of multi-source cross-validation in clinical ECG classification
Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of …
ECG Classification