First place solution for "medicine" topic in AI Challenge 2023

Diagnosing ‘silent’ heart attack using ECG waveforms (A Nightingale Open Science dataset)

My Research from the QAO REU 2024 Program

This project focuses on detecting atrial fibrillation (AFib) from ECG signals using machine learning techniques. Atrial fibrillation is a common heart arrhythmia that can lead to serious health issues. This project includes data preprocessing, feature extraction, and model training with a Random Forest Classifier to identify AFib efficiently.

This project implements 6 different privacy-preserving collaborative learning methods for training multi-institutional ECG classification models.

MIT-BIH ECG classification using 1D CNN with TensorFlow3

In this project, we will perform 12-lead ECG Multi-label Classification. Specifically, we will design a multi-model utilizing the characteristics of diagnoses from the Shaoxing and Ningbo databases.

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'.

This repository contains Medical Analysis projects

A Combined ResNet-DenseNet Architecture with ResU Blocks (ResU-Dense) for 12-lead ECG Abnormality Classification

ECG signal processing - Project A at the ECE Faculty at the Technion / Shahar & Yehonatan

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

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

Implement an intelligent diagnostic system capable of accurately classifying cardiac activity. By analyzing ECG images or electronic readings, the system aims to detect various abnormalities, including distinguishing normal vs. abnormal heartbeats, identifying myocardial infarction (MI) and its history, and assessing the impact of COVID-19.

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

Source code repository for the study: "Uncovering ECG Changes during Healthy Aging using Explainable AI"

MS and LVEF classification for ECG image using multi-task deep learning. Demo website (in Thai) ↓

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

his project involves the classification of ECG (Electrocardiogram) readings to determine whether they are normal or abnormal. The dataset consists of rows, each representing a complete ECG of a patient with 140 data points (readings).