{"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/convolutional-recurrent-neural-networks-for","title":"Convolutional Recurrent Neural Networks for Electrocardiogram Classification","arxiv_id":"1710.06122","date":"2017-10-17","proceeding":null,"authors":["Martin Zihlmann","Dmytro Perekrestenko","Michael Tschannen"],"abstract":"We propose two deep neural network architectures for classification of\narbitrary-length electrocardiogram (ECG) recordings and evaluate them on the\natrial fibrillation (AF) classification data set provided by the PhysioNet/CinC\nChallenge 2017. The first architecture is a deep convolutional neural network\n(CNN) with averaging-based feature aggregation across time. The second\narchitecture combines convolutional layers for feature extraction with\nlong-short term memory (LSTM) layers for temporal aggregation of features. As a\nkey ingredient of our training procedure we introduce a simple data\naugmentation scheme for ECG data and demonstrate its effectiveness in the AF\nclassification task at hand. The second architecture was found to outperform\nthe first one, obtaining an $F_1$ score of $82.1$% on the hidden challenge\ntesting set.","url_abs":"http://arxiv.org/abs/1710.06122v2","url_pdf":"http://arxiv.org/pdf/1710.06122v2.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":"convolutional-recurrent-neural-networks-for","repo_url":"https://github.com/yruffiner/ecg-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}