{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-open-source-toolbox-for-analysing-and","title":"An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave","arxiv_id":null,"date":"2014-09-24","proceeding":"Journal of Open Research Software 2014 9","authors":["Ikaro Silva","George Moody"],"abstract":"The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.","url_abs":"http://doi.org/10.5334/jors.bi","url_pdf":"https://openresearchsoftware.metajnl.com/articles/10.5334/jors.bi/galley/78/download/","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-open-source-toolbox-for-analysing-and","repo_url":"https://github.com/MIT-LCP/wfdb-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"an-open-source-toolbox-for-analysing-and","repo_url":"https://github.com/ikarosilva/wfdb-app-toolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"seizure-detection","task_name":"Seizure Detection"},{"task_slug":"sleep-stage-detection","task_name":"Sleep Stage Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/arrhythmia-detection-on-the-physionet","task":"Arrhythmia Detection","dataset":"The PhysioNet Computing in Cardiology Challenge 2017","model":"Feature-based approach (no segmentation)","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy (TEST-DB)":"79%","Accuracy (TRAIN-DB)":"72.0%"},"uses_additional_data":false},{"leaderboard":"/sota/arrhythmia-detection-on-the-physionet","task":"Arrhythmia Detection","dataset":"The PhysioNet Computing in Cardiology Challenge 2017","model":"Feature-based approach (10 s segments)","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy (TEST-DB)":"78%","Accuracy (TRAIN-DB)":"76.6%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}