{"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/encase-an-ensemble-classifier-for-ecg","title":"ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks","arxiv_id":null,"date":"2017-09-24","proceeding":"2017 Computing in Cardiology (CinC) 2017 9","authors":["Shenda Hong","Meng Wu","Yuxi Zhou","Qingyun Wang","Junyuan Shang","Hongyan Li","Junqing Xie"],"abstract":"We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. We first explore and implement expert features from statistical area, signal processing area and medical area. Then, we build DNNs to automatically extract deep features. Besides, we propose a new algorithm to find the most representative wave (called centerwave) among long ECG record, and extract features from centerwave. Finally, we combine these features together and put them into ensemble classifiers. Experiment on 4-class ECG data classification reports 0.84 F1 score, which is much better than any of the single model.","url_abs":"http://prucka.com/2017CinC/pdf/178-245.pdf","url_pdf":"http://prucka.com/2017CinC/pdf/178-245.pdf","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":"encase-an-ensemble-classifier-for-ecg","repo_url":"https://github.com/hsd1503/ENCASE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"ecg-classification","task_name":"ECG Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/arrhythmia-detection-on-the-physionet","task":"Arrhythmia Detection","dataset":"The PhysioNet Computing in Cardiology Challenge 2017","model":"ResNet + Expert Features","rank_in_archive_order":5,"of":5,"metrics":{"F1 (Hidden Test Set)":"0.825"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet-2017","task":"Time Series Classification","dataset":"Physionet 2017 Atrial Fibrillation","model":"ENCASE","rank_in_archive_order":2,"of":2,"metrics":{"F1 (Hidden Test Set)":"0.825"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}