Using deep learning to detect Atrial fibrillation
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Updated
Mar 7, 2019 - Jupyter Notebook
Using deep learning to detect Atrial fibrillation
Multi-label classfication of 8-leads ECGs.
Some simple experiments code of Biomedical.
A repository related to a master thesis in electronics, informatics and technology. Title: "Comparing Cardiological and Algorithm-Based ECG Interpretation in Athletes: Can Artificial Intelligence Improve the Algorithms?"
Public repository associated with: "Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort"
The repository contains the project https://github.com/mk590901/graph_widget: ported on Jetpack Compose the widget for visualization ECG.
Maps the STAFF III Database of ECG data annotations file from one line per patient to one line per file
User interface for Withings integration written in Angular
Non-Invasive point of care ECG signal detection and analytics for cardiac diseases
Classificação de séries temporais de sinais ECG com redes neurais convolucionais (CNN).
Used MATLAB to clean an ECG signal and extract useful information from it.
R peaks detection in ECGs using wavelet decomposition and higher statistics, implemented in MATLAB
Undergraduate group project in which we built an ECG classifier using a TCN-CNN with 97% accuracy
Python command line application used to denoise ECG data using wavelet transform, Savitky-Golay filter and Deep Neural Networks.
Empirical Mode Decomposition filter to remove the baseline frequency in ECG signal
Sensor learning project | AD8233 | ECG | ESP8266
3-point electrocardiogram using an Arduino
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