Unsupervised Identification and Replay-based Detection (UIRD) for New Category Anomaly Detection in ECG Signal
Fuente:
arXiv
Saved in:
| Main Authors: | Shi, Zhangyue, Wang, Zekai, Li, Yuxuan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing
by: Li, Yuxuan, et al.
Published: (2023)
by: Li, Yuxuan, et al.
Published: (2023)
An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals
by: Basora, Marc Garreta, et al.
Published: (2025)
by: Basora, Marc Garreta, et al.
Published: (2025)
Unsupervised Surrogate Anomaly Detection
by: Klüttermann, Simon, et al.
Published: (2025)
by: Klüttermann, Simon, et al.
Published: (2025)
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
by: Bei, Yuanchen, et al.
Published: (2024)
by: Bei, Yuanchen, et al.
Published: (2024)
Towards Unsupervised Validation of Anomaly-Detection Models
by: Idan, Lihi
Published: (2024)
by: Idan, Lihi
Published: (2024)
Unsupervised Symbolic Anomaly Detection
by: Hossain, Md Maruf, et al.
Published: (2026)
by: Hossain, Md Maruf, et al.
Published: (2026)
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
by: Li, Zhong, et al.
Published: (2025)
by: Li, Zhong, et al.
Published: (2025)
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
by: Üstek, İrem, et al.
Published: (2024)
by: Üstek, İrem, et al.
Published: (2024)
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
by: Xu, Hongzuo, et al.
Published: (2022)
by: Xu, Hongzuo, et al.
Published: (2022)
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
by: Fu, Yali, et al.
Published: (2025)
by: Fu, Yali, et al.
Published: (2025)
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection
by: Li, Jindong, et al.
Published: (2024)
by: Li, Jindong, et al.
Published: (2024)
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
by: Dai, Wei, et al.
Published: (2024)
by: Dai, Wei, et al.
Published: (2024)
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection
by: Masakuna, Jordan F., et al.
Published: (2024)
by: Masakuna, Jordan F., et al.
Published: (2024)
When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection
by: Kim, Dongmin, et al.
Published: (2023)
by: Kim, Dongmin, et al.
Published: (2023)
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection
by: Cao, Rui, et al.
Published: (2024)
by: Cao, Rui, et al.
Published: (2024)
Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
by: Zhang, Ruyi, et al.
Published: (2024)
by: Zhang, Ruyi, et al.
Published: (2024)
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
by: Ni, Hang, et al.
Published: (2025)
by: Ni, Hang, et al.
Published: (2025)
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
by: Tajgardan, Mohsen, et al.
Published: (2026)
by: Tajgardan, Mohsen, et al.
Published: (2026)
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series
by: Yuan, Hanyang, et al.
Published: (2024)
by: Yuan, Hanyang, et al.
Published: (2024)
GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration
by: Ding, Yihao, et al.
Published: (2026)
by: Ding, Yihao, et al.
Published: (2026)
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
by: Guo, Zhicheng, et al.
Published: (2023)
by: Guo, Zhicheng, et al.
Published: (2023)
Graph Neural Networks based Log Anomaly Detection and Explanation
by: Li, Zhong, et al.
Published: (2023)
by: Li, Zhong, et al.
Published: (2023)
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
by: Chen, Qingfeng, et al.
Published: (2025)
by: Chen, Qingfeng, et al.
Published: (2025)
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data
by: Ulmer, Markus, et al.
Published: (2023)
by: Ulmer, Markus, et al.
Published: (2023)
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection
by: Najafi, Seyed Amirhossein, et al.
Published: (2024)
by: Najafi, Seyed Amirhossein, et al.
Published: (2024)
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
by: Pinon, Nicolas, et al.
Published: (2025)
by: Pinon, Nicolas, et al.
Published: (2025)
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
by: Jiang, Xi, et al.
Published: (2024)
by: Jiang, Xi, et al.
Published: (2024)
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
by: Chen, Xiangwei, et al.
Published: (2024)
by: Chen, Xiangwei, et al.
Published: (2024)
Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
by: Zhong, Zhijie, et al.
Published: (2025)
by: Zhong, Zhijie, et al.
Published: (2025)
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
by: Anwar, Ahmed, et al.
Published: (2024)
by: Anwar, Ahmed, et al.
Published: (2024)
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
by: Huang, Wei, et al.
Published: (2026)
by: Huang, Wei, et al.
Published: (2026)
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
by: Malacarne, Sara, et al.
Published: (2026)
by: Malacarne, Sara, et al.
Published: (2026)
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
by: Gil, Natalia Martinez, et al.
Published: (2026)
by: Gil, Natalia Martinez, et al.
Published: (2026)
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
by: Li, Pengyu, et al.
Published: (2024)
by: Li, Pengyu, et al.
Published: (2024)
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
by: Huang, Tiange, et al.
Published: (2025)
by: Huang, Tiange, et al.
Published: (2025)
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
by: Li, Zhong, et al.
Published: (2025)
by: Li, Zhong, et al.
Published: (2025)
AnyECG-Lab: An Exploration Study of Fine-tuning an ECG Foundation Model to Estimate Laboratory Values from Single-Lead ECG Signals
by: Xiao, Yujie, et al.
Published: (2025)
by: Xiao, Yujie, et al.
Published: (2025)
Towards a Unified Framework of Clustering-based Anomaly Detection
by: Fang, Zeyu, et al.
Published: (2024)
by: Fang, Zeyu, et al.
Published: (2024)
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
by: Ghanim, Jihan, et al.
Published: (2024)
by: Ghanim, Jihan, et al.
Published: (2024)
Similar Items
-
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing
by: Li, Yuxuan, et al.
Published: (2023) -
An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals
by: Basora, Marc Garreta, et al.
Published: (2025) -
Unsupervised Surrogate Anomaly Detection
by: Klüttermann, Simon, et al.
Published: (2025) -
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
by: Bei, Yuanchen, et al.
Published: (2024) -
Towards Unsupervised Validation of Anomaly-Detection Models
by: Idan, Lihi
Published: (2024)