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

The equivalent of EEG-to-MIDI, but for ECG.

Heart rate detection from ECG signals with clinical validation

Converting real-time EEG into sounds, music and visual effects

Biosignal Processing in Python

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

Third year university IoT module for monitoring heart health in diabetic patients. The system combines hardware sensors, real-time data processing, and machine learning analytics to detect anomalies and track vital sign trends. The results are then displayed in a Streamlit web page.

Python API for Mentalab biosignal aquisition devices

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

ECG classification using MIT-BIH data, a deep CNN learning implementation of Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, https://www.nature.com/articles/s41591-018-0268-3 and also deploy the trained model to a web app using Flask, introduced at

Generate Realistic Deep-Fake ECGs

Sleep stage detection using ECG

A python command line tool to read an SCP-ECG file and print structure information

Biomedical signal (EEG/sEMG/ECG) completion/imputation using diffusion model. "A robust denoising diffusion framework for completing missing regions of multiple biomedical signals"

[NeurIPS 2025]PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

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

MobileNet1D: Lightweight ECG-based Biometric Identification Framework A PyTorch implementation for subject-disjoint ECG biometric identification on PTB-XL. Includes full preprocessing, MobileNet-1D model, AMP training, and evaluation scripts (AUC 99.6%, EER 2.1%).

Attention-based CNN-LSTM model - ICMLA 2024

A self-supervised foundation ECG model for broad and scalable cardiac applications