animikhaich / ECG-Atrial-Fibrillation-Classification-Using-CNN

This is a CNN based model which aims to automatically classify the ECG signals of a normal patient vs. a patient with AF and has been trained to achieve up to 93.33% validation accuracy.

Date Created 2019-03-29 (5 years ago)

Commits 8 (last one 3 years ago)

Stargazers 47 (0 this week)

Watchers 3 (0 this week)

Forks 19

License mit

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RepositoryStats indexes 579,238 repositories, of these animikhaich/ECG-Atrial-Fibrillation-Classification-Using-CNN is ranked #474,894 (18th percentile) for total stargazers, and #420,216 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #12,546/16,882.

animikhaich/ECG-Atrial-Fibrillation-Classification-Using-CNN is also tagged with popular topics, for these it's ranked: deep-learning (#7,132/8339), tensorflow (#2,028/2233), classification (#453/534)

Other Information

Homepage URL: http://animikh.me/

All Topics

ecg 1d-cnn patient cnn-keras ecg-signal tensorflow ecg-signals deep-learning 1d-convolution classification cnn-classification atrial-fibrillation

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updated: 2024-10-06 @ 09:36pm, id: 178456854 / R_kgDOCqMJFg