❤️ Predict heart disease risk with an advanced machine learning pipeline, featuring data preprocessing, model tuning, and an interactive web interface.

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

The ECG Analysis and Classification Project is an end-to-end framework for detecting cardiac conditions automatically from electrocardiogram (ECG) signals using machine learning and deep learning techniques. Built around the PTB Diagnostic ECG Database, this project integrates three main modules: Data Processing and Visualization – signal cleanin

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

Research on AI based ekg interpretation of myocardial infarction using multiple neural networks.

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

ECG Heartbeat Classification using Machine Learning and Deep Learning algorithms. Includes signal preprocessing, feature extraction, model comparison, and performance evaluation for arrhythmia detection using Python.

Case-based interpretable deep learning for ECG classification. This code implements ProtoECGNet from the following paper: "ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification." Sethi et al., MLHC 2025

Detecting atrial fibrillation from ECG signals

A machine learning project to detect and classify arrhythmias from ECG signals using Python, scikit-learn, and TensorFlow. Includes data preprocessing, model training, and evaluation.

A Method to Improve Any ECG Denoising Technique In limb leads

Customizing AI models for wearable ECG prototype - quantified health.

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.

This repository contains the source codes of the article published to detect changes in ECG caused by COVID-19 and automatically diagnose COVID-19 from ECG data.

Case-based interpretable deep learning for ECG classification. This code implements ProtoECGNet from the following paper: "ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification." Sethi et al., MLHC 2025

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.

Проект машинного обучения для анализа электрокардиограмм (ЭКГ) с использованием сиамских нейронных сетей для обучения с малым количеством примеров (few-shot learning). Этот проект реализует подход глубокого обучения для анализа сигналов ЭКГ и обнаружения сердечных аномалий.