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.

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

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).

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

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

This repository contains ML codebase developed during CSE713 group project

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.

Implementation of a Wavelet + BNN for Real-Time ECG Arrhythmia Detection on FPGA

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

Files of undergraduate thesis of group 180923_180939.

ECG Arrhythmia Detection using CNN + BiLSTM (Deep Learning Project)

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.