Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART ...

ABSTRACT: This paper studies recent assistive technologies and AI sound detection systems that have been developed to support both the safety and communication of individuals who are deaf. It ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve health care and medical research. Both ...

Abstract: Electrocardiogram (ECG) signals are the impulses generated by the heart which are used to analyze the proper functioning of heart. Our work deals with the efficient analysis of ...

Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use ...

This project applies Haar wavelet transform for ECG signal denoising and trains CNN/SVM classifiers to detect cardiac arrhythmias. Built using the MIT-BIH dataset, it's designed for research, learning ...

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

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

Abstract: One essential and often used tool for the diagnosis of cardiovascular disorders is the electrocardiogram (ECG). With the emergence of artificial intelligence (AI), it is possible to have ...