{"id":13612226,"url":"https://github.com/MousaviSajad/ECG-Heartbeat-Classification-seq2seq-model","last_synced_at":"2025-04-13T11:31:44.734Z","repository":{"id":40683836,"uuid":"170054028","full_name":"MousaviSajad/ECG-Heartbeat-Classification-seq2seq-model","owner":"MousaviSajad","description":"Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach","archived":false,"fork":false,"pushed_at":"2024-07-21T01:28:15.000Z","size":731,"stargazers_count":194,"open_issues_count":9,"forks_count":57,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-04-10T06:04:38.907Z","etag":null,"topics":["biosignals","cnn","deep-learning","ecg","ecg-heartbeat-classification","sequence-to-sequence","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MousaviSajad.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,"governance":null}},"created_at":"2019-02-11T02:18:24.000Z","updated_at":"2025-04-04T14:35:33.000Z","dependencies_parsed_at":"2023-10-21T12:17:00.265Z","dependency_job_id":null,"html_url":"https://github.com/MousaviSajad/ECG-Heartbeat-Classification-seq2seq-model","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MousaviSajad","download_url":"https://codeload.github.com/MousaviSajad/ECG-Heartbeat-Classification-seq2seq-model/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248705688,"owners_count":21148576,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["biosignals","cnn","deep-learning","ecg","ecg-heartbeat-classification","sequence-to-sequence","tensorflow"],"created_at":"2024-08-01T20:00:25.167Z","updated_at":"2025-04-13T11:31:44.188Z","avatar_url":"https://github.com/MousaviSajad.png","language":"Python","funding_links":[],"categories":["Code"],"sub_categories":["Repositories"],"readme":"# Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach\n\n# Paper\n Our paper can be downloaded from the [arxiv website](https://arxiv.org/pdf/1812.07421v2)\n * The Network architecture\n  ![Alt text](/images/seq2seq_b.jpg)\n \n## Requirements\n* Python 2.7\n* tensorflow/tensorflow-gpu\n* numpy\n* scipy\n* scikit-learn\n* matplotlib\n* imbalanced-learn (0.4.3)\n\n## Dataset\nWe evaluated our model using [the PhysioNet MIT-BIH Arrhythmia database](https://www.physionet.org/physiobank/database/mitdb/)\n* To download our pre-processed datasets use [this link](https://drive.google.com/drive/folders/19bDrAqlSGQuNLRmA-7pQRU9R81gSuY70?usp=sharing), then put them into the \"data\" folder.\n* Or you can follow the instructions of the readme file in the \"data preprocessing_Matlab\" folder to download the MIT-BIH database and perform data pre-processing. Then, put the pre-processed datasets into the \"data\" folder.\n\n## Train\n\n* Modify args settings in seq_seq_annot_aami.py for the intra-patient ECG heartbeat classification\n* Modify args settings in seq_seq_annot_DS1DS2.py for the inter-patient ECG heartbeat classification\n\n* Run each file to reproduce the model described in the paper, use:\n\n```\npython seq_seq_annot_aami.py --data_dir data/s2s_mitbih_aami --epochs 500\n```\n```\npython seq_seq_annot_DS1DS2.py --data_dir data/s2s_mitbih_aami_DS1DS2 --epochs 500\n```\n## Results\n  ![Alt text](/images/results.jpg)\n## Citation\nIf you find it useful, please cite our paper as follows:\n\n```\n@article{mousavi2018inter,\n  title={Inter-and intra-patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach},\n  author={Mousavi, Sajad and Afghah, Fatemeh},\n  journal={arXiv preprint arXiv:1812.07421},\n  year={2018}\n}\n```\n\n## References\n [deepschool.io](https://github.com/sachinruk/deepschool.io/blob/master/DL-Keras_Tensorflow)\n \n## Licence \nFor academtic and non-commercial usage \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMousaviSajad%2FECG-Heartbeat-Classification-seq2seq-model/lists"}