Identifying alterations in the cardiac ventricles using radiomics with an ensemble multi-classifier approach.
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Updated
Jan 8, 2022 - Python
Identifying alterations in the cardiac ventricles using radiomics with an ensemble multi-classifier approach.
Using deep learning to detect Atrial fibrillation
Takes data from the atrial fibrillation database from Physionet, and attempts to detect that atrial fibrillation using a number of statistical methods. Matlab code.
A convolutional neural network to detect atrial fibrillation from a single-lead ECG
💔 Global and Local Prediction in Automatic detection of Atrial Fibrillation
Atrial Fibrilation diagnosis based on the discriminative elements of an ensemble of GANs
Repository contains codes to run REACT mapping algorithm. REACT maps are a novel approach to identify organized islands in atrial fibrillation.
Takes data from the atrial fibrillation database from Physionet, and attempts to detect that atrial fibrillation using a number of statistical methods. Matlab code.
Special Project - CA classification (2019 Fall)
Feng et al. Front Cardiovasc Med. 2023 Oct 2;10:1189293. doi: 10.3389/fcvm.2023.1189293. eCollection 2023.
Basic ontology to represent the article: "Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation"
This system object can be used to detect Atrial Fibrillation in an ECG signal
Data Science Project - Time Series & Data Mining
Clinical usefulness of the SAMe-TT2R2 score to predict a poor TTR
A library for classifying single-lead ECG waveforms as either Normal Sinus Rhythm, Atrial Fibrillation, or Other Rhythm.
In this study we will introduce atrial fibrillation, one of the cardiac arrhythmias, and see how it can be diagnosed using convolutional neural networks in combination with various methods and supervised learning models, including: gray-level co-occurrence matrix, short-time Fourier transform based spectrogram, support-vector machines, k-nearest…
This repository contains code reproducing an existing method to detect atrial fibrillation using empirical mode decomposition of signals. This was a lecture that I gave for graduate-level BioSignal Processing course.
Code for the paper "Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation"
Software developed to carry out the End-of-Degree Project PRAFAI (Prediction of Recurrence of Atrial Fibrillation using Artificial Intelligence).
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