Mount Sinai analysis looks at the effectiveness of electrocardiograms analyzed via deep learning as a tool for early COPD detection ...

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality globally. Effective management ...

Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiogramsJournal: eBioMedicine ...

Abstract: In this paper we present fully automatic interpatient electrocardiogram (ECG) signal classification method using deep convolutional neural networks (CNN). ECG is simple and non-invasive way ...

The electrocardiogram (ECG) is an important tool for exploring the structure and function of the heart due to its low cost, ease of use, efficiency, and non-invasive nature. With the rapid development ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India Introduction: In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) ...

Add a description, image, and links to the ecg-representation-learning topic page so that developers can more easily learn about it.

Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...

Recently, Aircela, a fuel company headquartered in New York, publicly demonstrated a machine in Manhattan that produces gasoline directly from air. The event attracted city and state officials, ...

The model is trained solely on ECG signals using HRV and EDR features with an LSTM-based neural network.