A list of ECG/time series related papers and implementations. (Or anything I think it is relevant)
- Icentia11k: A Unsupervised Representation Learning Dataset For Arrhythmia Subtype Discovery, Shawn Tan et al., arXiv 2019
- Transfer Learning from Well-Curated to Less-Resourced Populations with HIV, Sonali Parbhoo et al., PMLR 2020
- CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients, Dani Kiyasseh et al., ICML 2021 | code
- Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL, Nils Strodthoff et al., IEEE Journal of Biomedical and Health Informatics 2021 | code
- Contrastive Heartbeats: Contrastive Learning For Self-supervised ECG Representation and Phenotyping, Crystal T. Wei et al., ICASSP 2022
- Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models, Temesgen Mehari et al., ML4H 2022 | code 🌟
- ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal, Han Yu et al., arXiv 2023
- Frozen Language Model Helps ECG Zero-Shot Learning, Jun Li et al., MIDL 2023
- Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection, Aofan Jiang et al., MICCAI 2023 | code
- Transfer Learning in ECG Diagnosis: Is It Effective?, Cuong V. Nguyen et al., arXiv 2024 | code
- Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning, Aofan Jiang et al., arXiv 2024
- ETP: Learning Transferable ECG Representations via ECG-Text Pre-training, Che Liu et al., ICASSP 2024
- Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram, Yeongyeon Na et al., ICLR 2024 | code
- Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, Jiehui Xu et al., ICLR 2022 | code
- Efficiently Modeling Long Sequences with Structured State Spaces, Albert Gu et al., ICLR 2022 | code 🌟
- A Time Series is Worth 64 Words: Long-term Forecasting with Transformers, Yuqi Nie et al., ICLR 2023 | code ⭐
- Effectively Modeling Time Series with Simple Discrete State Spaces, Michael Zhang et al., ICLR 2023 | code
- Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach, Xiang Lan et al., ICLR 2024 | code
- TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting, Defu Cao et al., ICLR 2024 | code1, code2
- Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data, Ayesha Vermani et al., ICLR 2024
- VQ-TR: Vector Quantized Attention for Time Series Forecasting, Kashif Rasul et al., ICLR 2024
- iTransformer: Inverted Transformers Are Effective for Time Series Forecasting, Yong Liu et al., ICLR 2024 | code
- Towards Transparent Time Series Forecasting, Krzysztof Kacprzyk et al., ICLR 2024
- REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning, Maxwell A. Xu et al., ICLR 2024 | code
- Soft Contrastive Learning for Time Series, Seunghan Lee et al., ICLR 2024 | code
- Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs, Ilan Naiman et al,. ICLR 2024 | code
- https://github.com/mondejar/ecg-classification
- https://github.com/MertDuman/Zero-Shot-ECG
- https://github.com/antonior92/automatic-ecg-diagnosis
- https://github.com/DL4mHealth/Contrastive-Learning-in-Medical-Time-Series-Survey
- https://github.com/liaoyuhua/LLM4TS
- https://github.com/agwy/ptb-xl/blob/main/PTB-XL.ipynb, PTB-XL data exploration
- https://github.com/madao33/ECG-Classfier/blob/main/code/dataPreprocess.ipynb, PTB-XL data processing
- https://github.com/helme/ecg_ptbxl_benchmarking, PTB-XL benchmarking
- https://github.com/torcheeg/torcheeg, a library built on PyTorch for EEG signal analysis
- https://github.com/DeepPSP/torch_ecg, ECG Deep Learning Framework Implemented using PyTorch
- https://github.com/klean2050/ecg-augmentations, ECG time-series augmentations library for PyTorch
- https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging
- NeuroKit:https://neuropsychology.github.io/NeuroKit/functions/ecg.html#