myocardial-infarction
There are 14 repositories under myocardial-infarction topic.
likith012/IMLE-Net
Official implementation of our IEEE:SMC 2021 paper "IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification"
degerliaysen/MultiEchoAI
Myocardial Infarction Detection
harshraj3223/Automated-Detection-and-Localization-of-Myocardial-Infarction-Research-Project
Classifier for detection and prediction of the type of MI or NORM from 12-lead ECG beats.
QJYBall/MyoPS-Net
MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR images
biodatlab/multitask-vision-ecg
MS and LVEF classification for ECG image using multi-task deep learning. Demo website (in Thai) ↓
AchmadFachturrohman/Machine-Learning-Based-Myocardial-Ischemia-Classification
Bachelor Thesis in Biomedical Engineering about Myocardial Ischemia Detection Using DWT Feature Extraction and Artificial Neural Networks Classifier
Phrungck/myocardial-segmentation
Use of segmentation models to extract the epicardium in low-quality echocardiograms. Dataset used: https://www.kaggle.com/datasets/aysendegerli/hmcqu-dataset
anasoubaha/DataAnalysis_Myocardial_Infarction
Statistical data analysis of the myocardial infarction for life or death prognosis using PCA method
molgenis/STEMI-scRNA-seq
single cell experiments in STEMI participants
danieljgorski/scRNAseq-Hmmr-OE
Hyaluronan Mediated Motility Receptor (Hmmr) overexpression
danieljgorski/spatial-24h
Spatial transcriptomics of cardiac tissue 24 h after ischemic injury
FIUPanther-JMolto98/EEL5813_PRJ03_MLP_Backpropagation_Momentum_EKG-MCI
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
matthiaszeller/GNN-MI
Graph Neural Networks for Mycardial Infarction Prediction
sidd1915/myocardial-infarction-using-ML
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.