{"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/ecg-arrhythmia-classification-using-a-2-d","title":"ECG arrhythmia classification using a 2-D convolutional neural network","arxiv_id":"1804.06812","date":"2018-04-18","proceeding":null,"authors":["Tae Joon Jun","Hoang Minh Nguyen","Daeyoun Kang","Dohyeun Kim","Daeyoung Kim","Young-Hak Kim"],"abstract":"In this paper, we propose an effective electrocardiogram (ECG) arrhythmia\nclassification method using a deep two-dimensional convolutional neural network\n(CNN) which recently shows outstanding performance in the field of pattern\nrecognition. Every ECG beat was transformed into a two-dimensional grayscale\nimage as an input data for the CNN classifier. Optimization of the proposed CNN\nclassifier includes various deep learning techniques such as batch\nnormalization, data augmentation, Xavier initialization, and dropout. In\naddition, we compared our proposed classifier with two well-known CNN models;\nAlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were\nused for the evaluation of the classifier. As a result, our classifier achieved\n99.05% average accuracy with 97.85% average sensitivity. To precisely validate\nour CNN classifier, 10-fold cross-validation was performed at the evaluation\nwhich involves every ECG recording as a test data. Our experimental results\nhave successfully validated that the proposed CNN classifier with the\ntransformed ECG images can achieve excellent classification accuracy without\nany manual pre-processing of the ECG signals such as noise filtering, feature\nextraction, and feature reduction.","url_abs":"http://arxiv.org/abs/1804.06812v1","url_pdf":"http://arxiv.org/pdf/1804.06812v1.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":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/Himansu97/ECG-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/ankur219/ECG-Arrhythmia-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/axelmukwena/biometricECG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/celiedel/ECG_Classification_with_2D_CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/lorenzobrusco/ECGNeuralNetwork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/lxdv/ecg-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/sonamghosh/pytorch-ecg-arrhythmia-classifier-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/HelloDuoLA/ECG-arrhythmia-classification-using-a-2-D-convolutional-neural-network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/MindCode-4/code-11/tree/main/ECG-arrhythmia-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/MindCode-4/code-6/tree/main/ECG-arrhythmia-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"ecg-arrhythmia-classification-using-a-2-d","repo_url":"https://github.com/qss878448059/ecg_mit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.06812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06812"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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