Giornale italiano di cardiologia (2006)
Sep 1, 2025
Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy ...
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. Although deep learning models have been widely applied to ECG classification tasks, their accuracy remains limited, especially i...
PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, he...
BACKGROUND: Perioperative electrocardiographic monitoring can offer immediate detection of myocardial ischaemia, yet its application in perioperative and remote monitoring settings is hampered by frequent false alarms and signal contamination. We per...
BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respiratory patterns during sleep. This disruption can have a cascading effect on the body, potentially leading to complications in various organs, including ...
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of these labels is strictly related to the statistics of cardiovascular diseases among the population, w...
Studies in health technology and informatics
Aug 7, 2025
In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collect heart activities, is used to diagnose cardiac conditions. Analyzing ...
Studies in health technology and informatics
Aug 7, 2025
Heart Rate Variability (HRV) is associated with diabetic complications. This analysis can quantify changes in heart rate variability, and it may help detect early alterations in diabetes. This study aimed to design and validate a Convolutional Neural...
This systematic review examines the transformative applications of empirical mode decomposition (EMD) in healthcare, focusing on its ability to analyse diverse physiological signals. By a thorough exploration of key databases and stringent study sele...
This research paper presents a systematic approach to ECG beat classification using advanced machine learning techniques. The study classifies ECG beats into six distinct classes based on annotations from the MIT-BIH Arrhythmia Database. The methodol...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.