AI may spot deadly heart risk hiding in a routine ECG. New research could help doctors find patients before cardiac arrest ...
aDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA bResearch Laboratory for Electronics, Massachusetts Institute of Technology ...
This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...
This repository includes the code of the ECG-DualNet for ECG classification proposed in the paper Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in ...
This repository presents an automated machine learning approach in Python to create a stress monitoring system with data from devices such as fitness trackers. With the rising popularity of trackers ...
Artificial intelligence (AI)–enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic ...
Basic information and contact details for the University of Technology, Iraq The University of Technology, Iraq is one of Iraq’s largest universities and is located on the Eastern side of the city of ...
Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to ...
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from ...
Representation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream ...
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