Flutter ECG application to Windows and Android.

Code for Optimized Arrhythmia Detection on Ultra-Edge Devices

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

Solving physionet2017 with RCRNN

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.

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).

Low-cost real-time ECG monitor using ESP32 + AD8232. FIR/IIR digital filters designed in MATLAB, implemented in C++ firmware. Detects Tachycardia, Bradycardia and irregular R-R intervals.

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

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

Deep Learning for ECG Arrhythmia Detection & Cardiac Event Prediction — 12-lead ECG classification with false alarm reduction for cardiac monitoring

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

ECG-TransNet implementation focused on the MIT-BIH Arrhythmia & AFDB datasets. Features a hybrid 1D-CNN & Transformer architecture for high-accuracy beat classification (AAMI standards). Includes specialized preprocessing for noise reduction, R-peak detection, and SHAP-based explainability for clinical validation of arrhythmia predictions.

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.

This repository contains ML codebase developed during CSE713 group project

Files of undergraduate thesis of group 180923_180939.

Classificação binária de arritmia em ECG (normal/anormal) com XGBoost + Optuna, comparando 9 cenários de ablação de features (sinal, estatísticas globais e regiões antes/depois do pico R).

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