[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
Deep Generative Models for Physiological Signals: A Systematic Literature Review
In this paper, we present a systematic literature review on deep generative models for
physiological signals, particularly electrocardiogram, electroencephalogram …
physiological signals, particularly electrocardiogram, electroencephalogram …
Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications
YX Guan, Y An, FY Guo, WB Pan, JX Wang - Chinese Medical Sciences …, 2023 - Elsevier
Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the
heart's electrical activity and provide valuable diagnostic clues about the health of the entire …
heart's electrical activity and provide valuable diagnostic clues about the health of the entire …
Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing
Extracting information from the electrocardiography (ECG) signal is an essential step in the
design of digital health technologies in cardiology. In recent years, several machine learning …
design of digital health technologies in cardiology. In recent years, several machine learning …
Atrial Fibrillation Detection from Ambulatory ECG with Accelerometry Contextualisation: A Semi-Supervised Learning Approach
Atrial fibrillation (AF) is a common cardiac arrhythmia causing severe complications if left
untreated. Due to its sporadic nature, early detection often requires longitudinal ambulatory …
untreated. Due to its sporadic nature, early detection often requires longitudinal ambulatory …