A machine-learning model trained on thousands of electrocardiogram recordings identifies a previously unrecognized group of at-risk people

AI model trained on routine ECGs detects hidden patterns linked to sudden cardiac death risk, UC Berkeley study finds

UC Berkeley researchers have developed an AI model trained on routine ECGs that can detect hidden patterns associated with sudden cardiac de

Harvard-Emory ECG Database v4.0: Large Collection of 12-Lead Electrocardiography Recordings

The Harvard-Emory ECG Database v4.0 is a comprehensive collection of 12-lead electrocardiography (ECG) recordings developed by researchers f

Medical records could be revealed by AI training-data vulnerability

LSM-2: Self-Supervised Learning for Incomplete Wearable Sensor Data in Health Monitoring

The article discusses LSM-2, a self-supervised learning approach for handling incomplete wearable sensor data in health monitoring. It addre

Foundation Models for Wearable Behavioral Data Improve Health Predictions

Researchers developed foundation models for behavioral data from wearable devices using over 2.5 billion hours of data from 162,000 individu

Cuffless hemodynamic monitoring with physics-informed machine learning models