medical

Myocardial infarction detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in myocardial-infarction-detection

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Dataset

Best Model

Actions

PTB dataset, ECG lead II

PTB Diagnostic ECG Database

Libraries

i

Use these libraries to find myocardial-infarction-detection models and implementations

Datasets

PTB Diagnostic ECG Database

Subtasks

No subtasks available.

Most implemented papers

ECG Heartbeat Classification: A Deep Transferable Representation

Mohammad Kachuee, Shayan Fazeli, M. Sarrafzadeh•Wed Apr 18 2018

A method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard is proposed.

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Early Myocardial Infarction Detection over Multi-view Echocardiography

S. Kiranyaz, M. Gabbouj, R. Mazhar, T. Hamid, A. Degerli•Mon Nov 08 2021

A pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views is proposed by creating a new benchmark dataset and improving the performance of the prior work of threshold-based APs by a Machine Learning based approach.

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Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography

D. Tran, Quang-Minh Nguyen, Hieu Pham, N. Tuan, Phi D. Nguyen, H. Pham, Thanh Le, Hanh Van, Bach Do, P. Tran, Vinh-Tuyen T Le, T. Nguyen, Long Tran•Sat Mar 11 2023

The proposed approach combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI and demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation model.

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Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms

Arjun Gupta, E. Huerta, Zhizhen Zhao, I. Moussa•Sat Sep 21 2019

This article studies the feasibility of using deep learning to identify suggestive electrocardiographic changes that may correctly classify heart conditions using the Physikalisch-Technische Bundesanstalt (PTB) database, and fine-tune the ConvNetQuake neural network model.

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Paper Graph

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