Flutter ECG application to Windows and Android.

Code for Optimized Arrhythmia Detection on Ultra-Edge Devices

Solving physionet2017 with RCRNN

Machine learning project on Distinguish between the presence and absence of cardiac arrhythmia and its classification in one of the 16 groups.

Code for CNNs based Explainable arrhythmia detection in federated settings

Code for deployed deep learning model in production for arrhythmia detection with Explainable AI

Arrhythmia detection using topological data analysis in combination with a convolutional neural network.

Medical-grade ECG arrhythmia detection with >95% sensitivity and >90% classification accuracy

A transformer custom tailored for arrhythmia detection and based on ECG (Electrocardiogram) signals.

Dual-channel CNN-LSTM for ECG arrhythmia classification (NSR, PAC, PVC) using raw ECG and derivative signals with Docker support

Newton–Puiseux for CVNNs: complete toolkit for uncertainty mining, confidence calibration and local symbolic-numeric analysis on ECG (MIT-BIH) and wireless IQ data (RadioML 2016.10A).

An end-to-end research project focused on detecting Premature Ventricular Contractions (PVC) using the Discrete Wavelet Transform DWT and a K-NN ML algorithm to classify ECGs between normal and PVC.

Files of undergraduate thesis of group 180923_180939.

This repository contains ML codebase developed during CSE713 group project

Evaluation of Deep Learning models for detecting irregular heartbeat rhythms (arrhythmias) on electrocardiogram (ECG) measurements.

A machine learning project leveraging ECG data to detect and classify cardiac arrhythmias. Features two models: a binary classifier for anomaly detection (Normal vs. Arrhythmia) and a multi-class classifier for specific arrhythmia types. Utilizes Random Forest, XGBoost, and other supervised algorithms with Boruta feature selection.

Web app for ECG-based arrhythmia prediction using a deep neural network trained on MIT-BIH data, with a clinical rules engine — built with Flask, TensorFlow, and Python.

Cardiac recordings plotting and analysis tool

Este proyecto se corresponde con la parte del código desarrollada para mi Trabajo de Fin de Grado, en la que realicé, mediante XAI, un análisis de los valores SHAP de un conjunto de variables del ECG para analizar cuáles eran más importantes en detección de arritmias con ML.