An electrocardiogram analysis foundation model.

Annotation of ECG signals using deep learning, tensorflow’ Keras

Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research

An asynchronous BLE Heart Monitor library with support for additional data from Polar monitors (ECG, accelerometers, etc)

This project is for Electrocardiogram(ECG) signal algorithms design and validation, include preprocessing, QRS-Complex detection, embedded system validation, ECG segmentation, label your machine learning dataset, and clinical trial...etc.

BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

A Unified Framework for Benchmarking Generative Electrocardiogram-Language Models (ELMs)

This is the project page for the iOS app ECG Recorder for Polar H10 heart rate sensor. The app records three types of data from a Polar H10 heart rate sensor: electrocardiogram (ECG), heart rate (HR) and the R-R interval (RR).

An ECG Foundation Model: Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners (ICML 2025)

Udacity AI for Healthcare Nanodegree Project: Heart Rate Estimation Algorithm From PPG and Accelerometer data

[MLHC 2025] ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling

Using deep learning to detect Atrial fibrillation

Fast and sample-accurate R-peak detectors based on Visisbility Graphs

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.

Code and Datasets for the paper "Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram", published on iScience in 2021.

Predicting driver stress levels using Physionet's SRAD (drivedb) dataset with methods such as LSTMs, RNNs, CNNs

Perform image and time series classification of various cardiovascular conditions as well as COVID-19 using Electrocardiogram data.

Predict Arterial Blood Pressure Using ECG and PPG Data

Official source code of "Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG"

A research-oriented training and evaluation framework for ECG-Language Models (ELMs)