mlsanityData to 7 Oct 2026

Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection

arXiv 2308.01639 ↗First posted 3 Aug 2023cs.CVcited by 10

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Abstract (arXiv)

Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering that no training dataset can exhaust all possible cardiac disorders. This paper proposes using anomaly detection to identify any unhealthy status, with normal ECGs solely for training. However, detecting anomalies in ECG can be challenging due to significant inter-individual differences and anomalies present in both global rhythm and local morphology. To address this challenge, this paper introduces a novel multi-scale cross-restoration framework for ECG anomaly detection and localization that considers both local and global ECG characteristics. The proposed framework employs a two-branch autoencoder to facilitate multi-scale feature learning through a masking and restoration process, with one branch focusing on global features from the entire ECG and the other on local features from heartbeat-level details, mimicking the diagnostic process of cardiologists. Anomalies are identified by their high restoration errors. To evaluate the performance on a large number of individuals, this paper introduces a new challenging benchmark with signal point-level ground truths annotated by experienced cardiologists. The proposed method demonstrates state-of-the-art performance on this benchmark and two other well-known ECG datasets. The benchmark dataset and source code are available at: urlhttps://github.com/MediaBrain-SJTU/ECGAD

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

10 of 30 references.

References · 10

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  3. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  4. 2022Diffusion Models for Medical Anomaly Detectioncited by 231
  5. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  6. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  7. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  8. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  9. 2017Robust, Deep and Inductive Anomaly Detectioncited by 24
  10. 2022Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalographycited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 2.

10 papers.

  1. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  2. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomizationcited by 6
  3. 2024TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detectioncited by 4
  4. 2024Complex Style Image Transformations for Domain Generalization in Medical Imagescited by 1
  5. 2023TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Networkcited by 0
  6. 2025SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Imagescited by 0
  7. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion modelscited by 0
  8. 2025Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptationcited by 0
  9. 2026Beyond Pooling: Matching for Robust Generalization under Data Heterogeneitycited by 0
  10. 2026Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosiscited by 0