Code repository of the paper: Beyond Supervision: Evaluating Contrastive Self-Supervised Learning Techniques for Electrocardiogram-Based Mental Stress Detection

This is the official repository for CardioLab. A machine and deep learning framework for the estimation and monitoring of laboratory abnormalities throught ECG data.

Demo of a smartwatch based systematic health monitoring solution designed for patients with chronic conditions

A Method to Improve Any ECG Denoising Technique In limb leads

Oloche's AI Cardiologist is a deep learning web app for real-time automated classification of cardiac arrhythmias from raw ECG signals. Uses a custom 1D CNN trained on MIT-BIH database to classify heartbeats into five categories with confidence scores and visualizations for diagnostic support.

A deep learning-based system for automatic detection of sleep apnea from ECG signals using a hybrid 1D CNN-BiLSTM architecture with an attention mechanism. Achieves high accuracy with minimal preprocessing, making it suitable for real-time, portable diagnostic applications.

ML-based solution for ECG signal processing and diagnosis classification

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

Clasificación de señales de Electrocardiogramas (ECG) mediante Deep Learning. Implementación basada y entrenada con el dataset MIT-BIH. Incluye una aplicación web interactiva con Flask.

Repository for the paper 'Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care'.

[Biomedical Signal Processing and Control] ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer

An ECG Monitoring System with Real-time Analysis for Tele-medecine facilites

Python classifier for the PhysioNet/Computing in Cardiology Challenge 2020

Comparison between classical and enanched genetic programming in signals classification (ECG). Project for Machine Learning, developed in 2025.

BioDG is a publically available framework for the evaluation of Domain Generalization algorithms in Biosignal Classification.

MIT-BIH ECG classification using 1D CNN with TensorFlow3

This project classifies ECG Signal as AF(Atrial Fibrillation) or Non-AF(All other rhythms).This project consist of 2 different models. A custom cnn and a transfer learning model. These model are doing the same thing with different approaches.

This repo contributes the usage of long term Atrial Fibrillation database to collect four types of cardiac arrhythmias data.

Implementation of a Self-ONN-based ECG classification model with feature injection, tested on the MIT-BIH Arrhythmia dataset.

ECG classification programs based on ML/DL methods