EchoNet-Dynamic is a deep learning model for assessing cardiac function in echocardiogram videos.

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

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'.

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

[CHIL 2024] Interpretation of Intracardiac Electrograms Through Textual Representations

Multimodal Transformer Networks with synchronised ECG and PCG data to detect and classify Cardiovascular Diseases

Python package for preprocessing OpenSlide image files and their corresponding annotations for use with Machine Learning segmentation models.

Detecting elevated hemodynamics from the 12-lead ECG alone

Pulse oximetry data processing and classification

Implementations of deep and other ML approaches for cardiology.

Francesco Soliani Master Thesis, SUNY Downstate Medical Center, Brooklyn (New York) https://www.linkedin.com/in/francesco-soliani-63ba22233/

Code for paper "Deciphering simultaneous heart conditions with spectrogram and explainable-AI approach".

a small python Library for calculating cardiovascular diseases risk using different clinically validated algorithms

Tutorial for building a dashboard with Plotly Dash to stream ECG data from Physionet

Analyze cardiograms with complex networks toolset to find predict disease

Département de cardiologie

The ECG Detection with Deep Learning project employs Convolutional Neural Networks to automatically analyze Electrocardiogram (ECG) data, facilitating precise detection of cardiac abnormalities and enhancing diagnostic accuracy.