mlsanityData to 7 Oct 2026

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

arXiv 2005.13249 ↗First posted 27 May 2020cs.LG eess.SP stat.MLcited by 170

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Abstract (arXiv)

The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another. We propose a family of contrastive learning methods, CLOCS, that encourages representations across space, time, textitand patients to be similar to one another. We show that CLOCS consistently outperforms the state-of-the-art methods, BYOL and SimCLR, when performing a linear evaluation of, and fine-tuning on, downstream tasks. We also show that CLOCS achieves strong generalization performance with only 25% of labelled training data. Furthermore, our training procedure naturally generates patient-specific representations that can be used to quantify patient-similarity.

Key facts. Cited by 170 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 11; 2023: 31; 2024: 40; 2025: 32; 2026: 43.

170 papers.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  2. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 113
  3. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  4. 2023Large-scale Training of Foundation Models for Wearable Biosignalscited by 89
  5. 2024ECG-FM: An Open Electrocardiogram Foundation Modelcited by 89
  6. 2024Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogramcited by 84
  7. 2021Self-supervised representation learning from 12-lead ECG datacited by 80
  8. 2024Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancementcited by 66
  9. 2024PaPaGei: Open Foundation Models for Optical Physiological Signalscited by 64
  10. 2023Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Seriescited by 59
  11. 2024Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classificationcited by 53
  12. 2021MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretationcited by 52
38 more
  1. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 52
  2. 2023Soft Contrastive Learning for Time Seriescited by 49
  3. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  4. 2023TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoderscited by 42
  5. 2022Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogramcited by 38
  6. 2024SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signalscited by 38
  7. 20213KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentationscited by 36
  8. 2022Contrastive Learning for Unsupervised Domain Adaptation of Time Seriescited by 36
  9. 2025Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Modelcited by 36
  10. 2021Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signalscited by 35
  11. 2025GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Imagescited by 35
  12. 2021Neighborhood Contrastive Learning Applied to Online Patient Monitoringcited by 32
  13. 2023ETP: Learning Transferable ECG Representations via ECG-Text Pre-trainingcited by 28
  14. 2022Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Datacited by 27
  15. 2024An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domainscited by 27
  16. 2024ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Textcited by 25
  17. 2021Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modelingcited by 24
  18. 2025CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Modelcited by 23
  19. 2023Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignalscited by 22
  20. 2022COCOA: Cross Modality Contrastive Learning for Sensor Datacited by 21
  21. 2023T-Rep: Representation Learning for Time Series using Time-Embeddingscited by 19
  22. 2023REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learningcited by 19
  23. 2023Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learningcited by 18
  24. 2024Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecturecited by 17
  25. 2024RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Datacited by 17
  26. 2023Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Seriescited by 16
  27. 2024Boosting Masked ECG-Text Auto-Encoders as Discriminative Learnerscited by 16
  28. 2023Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadatacited by 15
  29. 2024Wearable Accelerometer Foundation Models for Health via Knowledge Distillationcited by 15
  30. 2025From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretrainingcited by 15
  31. 2023Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imagingcited by 14
  32. 2024Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performancecited by 14
  33. 2023Event-Based Contrastive Learning for Medical Time Seriescited by 13
  34. 2021Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representationscited by 12
  35. 2021Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classificationcited by 12
  36. 2022Data Augmentation for Electrocardiogramscited by 12
  37. 2022Analysis of Augmentations for Contrastive ECG Representation Learningcited by 12
  38. 2021Contrastive Neural Processes for Self-Supervised Learningcited by 9

50 most cited of 170 All 170 (JSON)