{"url":"/task/ecg-classification","name":"ECG Classification","slug":"ecg-classification","description_markdown":null,"categories":[{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":116,"papers_with_code":49,"benchmarks":5,"benchmark_tables_in_archive":5,"benchmark_tables_shown":5,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":8,"subtasks":1,"parent_tasks":2},"benchmarks":[{"leaderboard":"/sota/ecg-classification-on-physionet-challenge-1","slug":"ecg-classification-on-physionet-challenge-1","dataset":"PhysioNet Challenge 2021","dataset_url":"/dataset/physionet-challenge-2021","rows_in_archive":5,"metrics":["PhysioNet Challenge score 2021"],"first_row_in_archive_order":{"model":"Local Lead Attention","paper_title":"Reading Between the Leads: Local Lead-Attention Based Classification of Electrocardiogram Signals","paper_url":"/paper/reading-between-the-leads-local-lead","paper_date":"2023-12-26","arxiv_id":null,"code_links":[{"title":"cph-cachet/LocalLeadAttention","url":"https://github.com/cph-cachet/LocalLeadAttention"}],"syntology":null}},{"leaderboard":"/sota/ecg-classification-on-electrocardiography-ecg","slug":"ecg-classification-on-electrocardiography-ecg","dataset":"Electrocardiography (ECG) on Telehealth Network of Minas Gerais (TNMG)","dataset_url":null,"rows_in_archive":4,"metrics":["F1 (1dAVb)","F1 (RBBB)","F1 (LBBB)","F1 (SB)","F1 (AF)","F1 (ST)"],"first_row_in_archive_order":{"model":"DNN","paper_title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","paper_url":"/paper/automatic-diagnosis-of-the-short-duration-12","paper_date":"2019-04-02","arxiv_id":"1904.01949","code_links":[{"title":"antonior92/automatic-ecg-diagnosis","url":"https://github.com/antonior92/automatic-ecg-diagnosis"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/ecg-classification-on-physionet-challenge","slug":"ecg-classification-on-physionet-challenge","dataset":"PhysioNet Challenge 2020","dataset_url":"/dataset/physionet-challenge-2020","rows_in_archive":2,"metrics":["Accuracy(stratified10-fold)","F1(stratified10-fold)","F2(stratified10-fold)","G2(stratified10-fold)","PhysioNet/CinC Challenge Score(stratified10-fold)","PhysioNet Challenge score (test data)","PhysioNet Challenge score 2020 (validation data)"],"first_row_in_archive_order":{"model":"1D CNN Encoder","paper_title":"Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs","paper_url":"/paper/convolutional-neural-network-and-rule-based","paper_date":"2020-12-31","arxiv_id":null,"code_links":[{"title":"Bsingstad/PhysioNet-CinC-Challenge2020-TeamUIO","url":"https://github.com/Bsingstad/PhysioNet-CinC-Challenge2020-TeamUIO"}],"syntology":null}},{"leaderboard":"/sota/ecg-classification-on-ptb-xl","slug":"ecg-classification-on-ptb-xl","dataset":"PTB-XL","dataset_url":"/dataset/ptb-xl","rows_in_archive":1,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"xresnet1d101","paper_title":"Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL","paper_url":"/paper/deep-learning-for-ecg-analysis-benchmarks-and","paper_date":"2020-04-28","arxiv_id":"2004.13701","code_links":[{"title":"helme/ecg_ptbxl_benchmarking","url":"https://github.com/helme/ecg_ptbxl_benchmarking"},{"title":"dthiagarajan/ptb-xl-research","url":"https://github.com/dthiagarajan/ptb-xl-research"}],"syntology":null}},{"leaderboard":"/sota/ecg-classification-on-ucr-time-series","slug":"ecg-classification-on-ucr-time-series","dataset":"UCR Time Series Classification Archive","dataset_url":"/dataset/ucr-time-series-classification-archive","rows_in_archive":1,"metrics":["Accuracy (Test)"],"first_row_in_archive_order":{"model":"V2Sa","paper_title":"Voice2Series: Reprogramming Acoustic Models for Time Series Classification","paper_url":"/paper/voice2series-reprogramming-acoustic-models","paper_date":"2021-06-17","arxiv_id":"2106.09296","code_links":[{"title":"huckiyang/Voice2Series-Reprogramming","url":"https://github.com/huckiyang/Voice2Series-Reprogramming"},{"title":"srijith-rkr/kaust-whisper-adapter","url":"https://github.com/srijith-rkr/kaust-whisper-adapter"},{"title":"dodohow1011/speechadvreprogram","url":"https://github.com/dodohow1011/speechadvreprogram"}],"syntology":{"n":7,"n_ran":1,"n_unverified":6,"n_pointer_only":3}}}],"datasets":[{"url":"/dataset/ucr-time-series-classification-archive","name":"UCR Time Series Classification Archive","full_name":"UCR Time Series Classification Archive","num_papers_in_archive":42},{"url":"/dataset/ptb","name":"PTB Diagnostic ECG Database","full_name":"","num_papers_in_archive":22},{"url":"/dataset/ecg-heartbeat-categorization-dataset","name":"ECG Heartbeat Categorization Dataset","full_name":"","num_papers_in_archive":10},{"url":"/dataset/ptb-xl","name":"PTB-XL","full_name":"","num_papers_in_archive":7},{"url":"/dataset/code-15","name":"CODE-15%","full_name":"","num_papers_in_archive":6},{"url":"/dataset/physionet-challenge-2021","name":"PhysioNet Challenge 2021","full_name":"The PhysioNet/Computing in Cardiology Challenge 2021","num_papers_in_archive":6},{"url":"/dataset/physionet-challenge-2020","name":"PhysioNet Challenge 2020","full_name":"PhysioNet Challenge 2020","num_papers_in_archive":4},{"url":"/dataset/mimic-iv-ecg","name":"MIMIC-IV-ECG","full_name":"MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset","num_papers_in_archive":3}],"subtasks":[{"url":"/task/photoplethysmography-ppg","name":"Photoplethysmography (PPG)"}],"parent_tasks":[{"url":"/task/blood-pressure-estimation","name":"Blood pressure estimation"},{"url":"/task/electrocardiography-ecg","name":"Electrocardiography (ECG)"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":49,"tagged_in_all":116,"items":[{"url":"/paper/voice2series-reprogramming-acoustic-models","title":"Voice2Series: Reprogramming Acoustic Models for Time Series Classification","date":"2021-06-17","arxiv_id":"2106.09296","repositories_listed":3,"syntology":{"n":7,"n_ran":1,"n_unverified":6,"n_pointer_only":3}},{"url":"/paper/zero-shot-ecg-classification-with-multimodal","title":"Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement","date":"2024-03-11","arxiv_id":"2403.06659","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/anomaly-detection-in-time-series-with-triadic","title":"Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification","date":"2020-12-09","arxiv_id":"2012.04936","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-ecg-analysis-benchmarks-and","title":"Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL","date":"2020-04-28","arxiv_id":"2004.13701","repositories_listed":2,"syntology":null},{"url":"/paper/towards-understanding-ecg-rhythm","title":"Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings","date":"2018-08-17","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-ecg-classification","title":"Deep Learning for ECG Classification","date":"2017-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/cardioformer-advancing-ai-in-ecg-analysis","title":"Cardioformer: Advancing AI in ECG Analysis with Multi-Granularity Patching and ResNet","date":"2025-05-08","arxiv_id":"2505.05538","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-adversarial-attacks-on-ecg","title":"Mitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial Training","date":"2025-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/protoecgnet-case-based-interpretable-deep","title":"ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning","date":"2025-04-11","arxiv_id":"2504.08713","repositories_listed":1,"syntology":null},{"url":"/paper/dcentnet-decentralized-multistage-biomedical","title":"DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits","date":"2025-01-31","arxiv_id":"2502.17446","repositories_listed":1,"syntology":null},{"url":"/paper/gaf-fusionnet-multimodal-ecg-analysis-via","title":"GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention","date":"2024-12-07","arxiv_id":"2501.01960","repositories_listed":1,"syntology":null},{"url":"/paper/learning-general-representation-of-12-lead","title":"Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture","date":"2024-10-11","arxiv_id":"2410.08559","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/heartbeat-classification-using-various","title":"Heartbeat classification using various machine learning models: A comparative study","date":"2024-09-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/open-world-electrocardiogram-classification","title":"Open-World Electrocardiogram Classification via Domain Knowledge-Driven Contrastive Learning","date":"2024-07-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-resolution-mutual-learning-network","title":"A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification","date":"2024-06-12","arxiv_id":"2406.16928","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-and-differential-privacy-1","title":"Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data","date":"2024-04-26","arxiv_id":"2405.00725","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-investigation-of-multi-source-cross","title":"Empirical investigation of multi-source cross-validation in clinical ECG classification","date":"2024-03-22","arxiv_id":"2403.15012","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-in-ecg-diagnosis-is-it","title":"Transfer Learning in ECG Diagnosis: Is It Effective?","date":"2024-02-03","arxiv_id":"2402.02021","repositories_listed":1,"syntology":null},{"url":"/paper/guiding-masked-representation-learning-to","title":"Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram","date":"2024-02-02","arxiv_id":"2402.09450","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_unverified":3,"n_pointer_only":12}},{"url":"/paper/reading-between-the-leads-local-lead","title":"Reading Between the Leads: Local Lead-Attention Based Classification of Electrocardiogram Signals","date":"2023-12-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/automatic-ecg-classification-using-discrete","title":"Automatic ECG classification using discrete wavelet transform and one-dimensional convolutional neural network","date":"2023-12-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mpcnn-a-novel-matrix-profile-approach-for-cnn","title":"MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification","date":"2023-11-25","arxiv_id":"2311.15041","repositories_listed":1,"syntology":null},{"url":"/paper/melep-a-novel-predictive-measure-of","title":"MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis","date":"2023-10-27","arxiv_id":"2311.04224","repositories_listed":1,"syntology":null},{"url":"/paper/arrhythmia-classifier-based-on-ultra","title":"Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network","date":"2023-04-04","arxiv_id":"2304.01568","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-classification-system-for-arrhythmia","title":"ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks","date":"2023-03-07","arxiv_id":"2303.03660","repositories_listed":1,"syntology":null},{"url":"/paper/advancing-the-state-of-the-art-for-ecg","title":"Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models","date":"2022-11-14","arxiv_id":"2211.07579","repositories_listed":1,"syntology":null},{"url":"/paper/multimodality-multi-lead-ecg-arrhythmia","title":"Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning","date":"2022-09-30","arxiv_id":"2210.06297","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-deep-learning-based-3-lead-ecg","title":"Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration","date":"2022-08-15","arxiv_id":"2208.07088","repositories_listed":1,"syntology":null},{"url":"/paper/lightx3ecg-a-lightweight-and-explainable-deep","title":"LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification","date":"2022-07-25","arxiv_id":"2207.12381","repositories_listed":1,"syntology":null},{"url":"/paper/decorrelative-network-architecture-for-robust","title":"Decorrelative Network Architecture for Robust Electrocardiogram Classification","date":"2022-07-19","arxiv_id":"2207.09031","repositories_listed":1,"syntology":null}],"syntology_records":4,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}