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ECGAnalysisDashbaord

An Web Based interactive ECG Analysis Dashboard

bandicam.2024-08-19.11-10-01-202.mp4

  • Preproceesing

  • Descriptive Analysis

  • Arythmia Detection

  • Myocardiac Infaction Detection

  • Person Identification

    Made with Streamlit with State of the Art Models

  1. Seják, M., Sido, J., & Žahour, D. (2023). ElectroCardioGuard: Preventing patient misidentification in electrocardiogram databases through neural networks. Knowledge-based Systems, 280, 111014. https://doi.org/10.1016/j.knosys.2023.111014

  2. Gupta, A., Huerta, E., Zhao, Z., & Moussa, I. (2021). Deep Learning for Cardiologist-Level Myocardial Infarction Detection in Electrocardiograms. IFMBE Proceedings, 80, 341–355. https://doi.org/10.1007/978-3-030-64610-3_40

  3. Mousavi, S., & Afghah, F. (2019). Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1308–1312. https://doi.org/10.1109/ICASSP.2019.8683140

  4. Al-Jibreen, A., Al-Ahmadi, S., Islam, S., & Artoli, A. M. (2024). Person identification with arrhythmic ECG signals using deep convolution neural network. Scientific Reports, 14(1), 4431. https://doi.org/10.1038/s41598-024-55066-w

  5. Butt, F. S., Wagner, M. F., Schafer, J., & Ullate, D. G. (2022). Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals. IEEE Access, 10, 118601–118616. https://doi.org/10.1109/ACCESS.2022.3220670 https://github.com/user-attachments/assets/c0171626-a432-4aa9-8704-7f63f8e19b51