Learn how machine learning ECG fatigue detection achieved 97.96% accuracy in older adults using a machine learning model.

Smart digital health has reshaped patient monitoring, but it faces a fundamental trade-off between device intelligence and continuous, energy-efficient monitoring. Inspired by self-sustaining ...

According to Future Market Insights, the AI-Driven Cardiac Diagnostics Market is valued at USD 903.2 million in 2026 and is projected to reach USD ...

Sudden cardiac death kills about 300,000 people in the U.S. each year, even though implantable defibrillators have been able to stop many lethal arrhythmias for decades. The main issue today isn’t in ...

TORONTO, ON / ACCESS Newswire / June 23, 2026 / AI/ML Innovations Inc. ("AIML" or the "Company") (CSE:AIML)(OTCQB:AIMLF)(FWB:42FB), a leader in AI-powered physiological signal interpretation, is ...

This review aims to identify the key barriers to clinical application of Machine Learning (ML) in multi-class voice disorder classification. A comprehensive scoping review of research published ...

Abstract: In this article, we propose and comparatively evaluate a system for inferring electrocardiogram (ECG) signal waveforms from chest wall displacement recorded by a wearable non-contact ...

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 ...

Abstract: This paper proposes a scalable, interpretable automated system for detecting arrhythmias in single-lead electrocardiogram (ECG) signals. The pipeline creates timefrequency representations of ...

This repository contains the complete workflow for automated textile fiber classification using Fourier-Transform Infrared (FTIR) spectroscopy. The project implements multiple preprocessing pipelines ...