{"id":16271940,"url":"https://github.com/horseee/physionet","last_synced_at":"2025-09-06T13:32:06.349Z","repository":{"id":105026168,"uuid":"137024743","full_name":"horseee/PhysioNet","owner":"horseee","description":"Deep learning based ECG classification","archived":false,"fork":false,"pushed_at":"2018-06-25T05:51:28.000Z","size":32,"stargazers_count":17,"open_issues_count":2,"forks_count":3,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-12-29T13:45:26.318Z","etag":null,"topics":["deep-learning","ecg","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/horseee.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2018-06-12T06:06:50.000Z","updated_at":"2023-12-29T13:35:55.000Z","dependencies_parsed_at":"2023-05-25T04:30:29.531Z","dependency_job_id":null,"html_url":"https://github.com/horseee/PhysioNet","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/horseee%2FPhysioNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/horseee%2FPhysioNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/horseee%2FPhysioNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/horseee%2FPhysioNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/horseee","download_url":"https://codeload.github.com/horseee/PhysioNet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":232126160,"owners_count":18476190,"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":["deep-learning","ecg","tensorflow"],"created_at":"2024-10-10T18:15:26.417Z","updated_at":"2025-01-01T20:53:47.871Z","avatar_url":"https://github.com/horseee.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## ECG classification\n\n#### Dataset\n[physionet challenge 2017](https://www.physionet.org/challenge/2017/)  \n![vis](https://github.com/VainF/PhysioNet/blob/master/imgs/data.png)  \n\n#### Requirements\n* tensorflow\n* numpy\n* scipy\n* pandas  \nAlso, you can use the command `pip3 install -r requirements.txt` to install the dependency packages.  \nIn this project, both python2 and python3 are ok(But we strongly suggest that you use python3).\n\n#### How to Run\n1. Put the data set in folder.\n2. Run `merge_dataset.py` to create **train.mat** and **test.mat**. Use the following command to run the code.    \n```\npython3 merge_dataset.py --dir YOUR_TRAINING_SET_FOLDER_NAME\n```  \nUse `python3 merge_dataset.py -h` if you need some help.    \n3. Run `train.py`. You can choose your parameter for the following parameters in your command.  \n   * learning_rate \n   * epochs\n   * batch_size.\n   * k_folder: True/False.   \n\n   If you want to begin the process for k-folder validation, use the following command: `python3 train.py --k_folder True`. If you only want to train the model, use the command: `python3 train.py`.\nUse `python3 train.py -h` if you need some help.  \n   \n4. After you train the model, use `test.py` to test the accuracy and F1 rate. The default path for checkpoints is **checkpoints/**. If you use other path, run the test.py use the following command:\n```\npython3 test.py --check_point_folder YOUR_CHECKPOINT_FOLDER_PATH\n```\n\n\n#### Experiment result\nThe F1 for our model is **0.82**. But maybe if you run you will get a different number for that the training and testing set is randomly choose.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhorseee%2Fphysionet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhorseee%2Fphysionet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhorseee%2Fphysionet/lists"}