{"id":42649141,"url":"https://github.com/fernandoandreotti/cinc-challenge2017","last_synced_at":"2026-01-29T07:18:45.080Z","repository":{"id":54417997,"uuid":"103513361","full_name":"fernandoandreotti/cinc-challenge2017","owner":"fernandoandreotti","description":"ECG classification from short single lead segments (Computing in Cardiology Challenge 2017 entry)","archived":false,"fork":false,"pushed_at":"2019-11-07T20:25:10.000Z","size":21018,"stargazers_count":146,"open_issues_count":1,"forks_count":70,"subscribers_count":12,"default_branch":"master","last_synced_at":"2024-02-01T08:51:58.492Z","etag":null,"topics":["arrhythmia","cardiology","challenge","classification","convolutional-neural-networks","deep-convolutional-networks","ecg","physionet"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fernandoandreotti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-09-14T09:26:10.000Z","updated_at":"2024-01-19T12:55:30.000Z","dependencies_parsed_at":"2022-08-13T15:00:57.306Z","dependency_job_id":null,"html_url":"https://github.com/fernandoandreotti/cinc-challenge2017","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/fernandoandreotti/cinc-challenge2017","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandoandreotti%2Fcinc-challenge2017","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandoandreotti%2Fcinc-challenge2017/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandoandreotti%2Fcinc-challenge2017/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandoandreotti%2Fcinc-challenge2017/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fernandoandreotti","download_url":"https://codeload.github.com/fernandoandreotti/cinc-challenge2017/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandoandreotti%2Fcinc-challenge2017/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28869082,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-29T06:56:44.678Z","status":"ssl_error","status_checked_at":"2026-01-29T06:56:35.794Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["arrhythmia","cardiology","challenge","classification","convolutional-neural-networks","deep-convolutional-networks","ecg","physionet"],"created_at":"2026-01-29T07:18:44.393Z","updated_at":"2026-01-29T07:18:45.072Z","avatar_url":"https://github.com/fernandoandreotti.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![license](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](./LICENSE)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/comparing-feature-based-classifiers-and/arrhythmia-detection-on-the-physionet)](https://paperswithcode.com/sota/arrhythmia-detection-on-the-physionet?p=comparing-feature-based-classifiers-and)\n\n## ECG classification from single-lead segments using _Deep Convolutional Neural Networks_ and _Feature-Based Approaches_\n\n#### Our entry for the Computing in Cardiology Challenge 2017: Atrial Fibrillation (AF) Classification from a short single lead Electrocardiogram (ECG) recording\n\nWhen using this code, please cite [our paper](http://prucka.com/2017CinC/pdf/360-239.pdf): \n\n\u003e Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., \u0026 De Vos, M. (2017). Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In Computing in Cardiology. Rennes (France).\n\n\nThis repository contains our solution [1] to the Physionet Challenge 2017 presented at the Computing in Cardiology conference 2017. As part of the Challenge, based on short single-lead ECG segments with 10-60 seconds duration, the classifier should output one of the following classes:\n\n| Class  | Description |\n| ----- | -------------------:|\n| N | normal sinus rhythm |\n| A | atrial fibrillation (AF) |\n| O | other cardiac rhythms |\n| ~ | noise segment |\n\n\nTwo methodologies are proposed and described in distict forlder within this repo:\n\n* Classic feature-based MATLAB approach (`featurebased-approach` folder)\n* Deep Convolutional Network Approach in Python (`deeplearn-approach` folder)\n\n\n## Downloading Challenge data\n\nFor downloading the [challenge training set](https://physionet.org/challenge/2017/training2017.zip). This can be done on Linux using:\n\n```bash\nwget https://physionet.org/challenge/2017/training2017.zip\nunzip training2017.zip\n```\n\n## Acknowledgment\nAll authors are affilated at the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford.\n\n\n## License\n\nReleased under the GNU General Public License v3\n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.\n\n## References\n\nWhen using this code, please cite [1].\n\n[1]: Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., \u0026 De Vos, M. (2017). Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In Computing in Cardiology. Rennes (France).\n\n[2]: Clifford, G.D., Liu, C., Moody, B., Silva, I., Li, Q., Johnson, A.E.W., \u0026 Mark, R.G. (2017). AF Classification from a Short Single Lead ECG Recording: the PhysioNet Computing in Cardiology Challenge 2017. In Computing in Cardiology. Rennes (France).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffernandoandreotti%2Fcinc-challenge2017","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffernandoandreotti%2Fcinc-challenge2017","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffernandoandreotti%2Fcinc-challenge2017/lists"}