Copertina

Enhancing Imbalanced Electrocardiogram Classification: A Novel Approach Integrating Data Augmentation through Wavelet Transform and Interclass Fusion

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Shao, Haijian, Liu, Wei, Deng, Xing, Lu, Daze
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Electrocardiogram Classification with Transformers Using Koopman and Wavelet Features
di: Ghosh, Sucheta, et al.
Pubblicazione: (2026)

Masked Transformer for Electrocardiogram Classification
di: Zhou, Ya, et al.
Pubblicazione: (2023)

EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification
di: Deng, Hanhui, et al.
Pubblicazione: (2025)

Data Augmentation for Classification of Negative Pregnancy Outcomes in Imbalanced Data
di: Biswas, Md Badsha
Pubblicazione: (2025)

LieTrunc-QNN: Lie Algebra Truncation and Quantum Expressivity Phase Transition from LiePrune to Provably Stable Quantum Neural Networks
di: Shao, Haijian, et al.
Pubblicazione: (2026)

Hierarchical Transformer for Electrocardiogram Diagnosis
di: Tang, Xiaoya, et al.
Pubblicazione: (2024)

The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering
di: Plagwitz, Lucas, et al.
Pubblicazione: (2024)

Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis
di: Hu, Shuaicong, et al.
Pubblicazione: (2024)

Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification
di: Oppelt, Maximilian P, et al.
Pubblicazione: (2020)

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
di: Somón, Elvin, et al.
Pubblicazione: (2026)

A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
di: Malialis, Kleanthis, et al.
Pubblicazione: (2022)

Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
di: Tang, Jialu, et al.
Pubblicazione: (2024)

Enhancing Electrocardiogram Signal Analysis Using NLP-Inspired Techniques: A Novel Approach with Embedding and Self-Attention
di: Ganguly, Prapti, et al.
Pubblicazione: (2024)

Data Augmentation with Variational Autoencoder for Imbalanced Dataset
di: Stocksieker, Samuel, et al.
Pubblicazione: (2024)

ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
di: Chen, Huangxun, et al.
Pubblicazione: (2019)

Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data
di: Sun, Xu, et al.
Pubblicazione: (2024)

ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
di: Thapaliya, Bishal, et al.
Pubblicazione: (2024)

Decorrelative Network Architecture for Robust Electrocardiogram Classification
di: Wiedeman, Christopher, et al.
Pubblicazione: (2022)

Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification
di: Du, Wumei, et al.
Pubblicazione: (2024)

Learning Confidence Bounds for Classification with Imbalanced Data
di: Clifford, Matt, et al.
Pubblicazione: (2024)

A Pipeline of Augmentation and Sequence Embedding for Classification of Imbalanced Network Traffic
di: Shokri, Matin, et al.
Pubblicazione: (2025)

Time Series Data Augmentation as an Imbalanced Learning Problem
di: Cerqueira, Vitor, et al.
Pubblicazione: (2024)

Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
di: Li, Wenjie, et al.
Pubblicazione: (2025)

ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification
di: Gazeran, Mahsa, et al.
Pubblicazione: (2026)

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach
di: Tanveer, M., et al.
Pubblicazione: (2024)

Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning
di: da Silva, Matheus Camilo, et al.
Pubblicazione: (2026)

Compact Neural Network Algorithm for Electrocardiogram Classification
di: Frausto-Avila, Mateo, et al.
Pubblicazione: (2024)

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
di: Cha, Suman, et al.
Pubblicazione: (2025)

Histogram Approaches for Imbalanced Data Streams Regression
di: Aminian, Ehsan, et al.
Pubblicazione: (2025)

Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes
di: Liu, Sishun, et al.
Pubblicazione: (2025)

Imbalanced Classification through the Lens of Spurious Correlations
di: Hackstein, Jakob, et al.
Pubblicazione: (2025)

MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning
di: Hwang, Seong-Hyeon, et al.
Pubblicazione: (2025)

Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals
di: Wang, Tiezhi, et al.
Pubblicazione: (2025)

A Time-Series Data Augmentation Model through Diffusion and Transformer Integration
di: Zhang, Yuren, et al.
Pubblicazione: (2025)

iHHO-SMOTe: A Cleansed Approach for Handling Outliers and Reducing Noise to Improve Imbalanced Data Classification
di: Raslan, Khaled SH., et al.
Pubblicazione: (2025)

Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
di: Shao, Kaixuan, et al.
Pubblicazione: (2026)

Classification Imbalance as Transfer Learning
di: Xia, Eric, et al.
Pubblicazione: (2026)

Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation
di: De La Fuente, Neil, et al.
Pubblicazione: (2024)

A Bilevel Optimization Framework for Imbalanced Data Classification
di: Medlin, Karen, et al.
Pubblicazione: (2024)

GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
di: Wang, Zhixiao, et al.
Pubblicazione: (2026)