There are 2 repositories under myocardial-infarction topic.
Official implementation of our IEEE:SMC 2021 paper "IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification"
Myocardial Infarction Detection
Classifier for detection and prediction of the type of MI or NORM from 12-lead ECG beats.
MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR images
MS and LVEF classification for ECG image using multi-task deep learning. Demo website (in Thai) ↓
Bachelor Thesis in Biomedical Engineering about Myocardial Ischemia Detection Using DWT Feature Extraction and Artificial Neural Networks Classifier
Use of segmentation models to extract the epicardium in low-quality echocardiograms. Dataset used: https://www.kaggle.com/datasets/aysendegerli/hmcqu-dataset
Statistical data analysis of the myocardial infarction for life or death prognosis using PCA method
single cell experiments in STEMI participants
Hyaluronan Mediated Motility Receptor (Hmmr) overexpression
Spatial transcriptomics of cardiac tissue 24 h after ischemic injury
Repository contains my MATLAB files for the hand-coded Myocardial-Infarction detection model trained on EKG data whose features were carefully engineered for the EEL5813 - Neural Networks: Algorithms and Applications course, PROJECT03
Graph Neural Networks for Mycardial Infarction Prediction
This project focuses on the detection of myocardial infarction (heart attack) using machine learning techniques. We leverage the PTB diagnostic database, a widely used dataset in the field of cardiology.