Electrocardiogram monitoring wearable devices and artificial-intelligence-enabled diagnostic capabilities: a review
L Neri, MT Oberdier, KCJ van Abeelen, L Menghini… - Sensors, 2023 - mdpi.com
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-
risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions …
risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions …
[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 …
Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure
Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial
for preventing and diagnosing hypertension related diseases. However, current continuous …
for preventing and diagnosing hypertension related diseases. However, current continuous …
A deep learning approach for atrial fibrillation classification using multi-feature time series data from ecg and ppg
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …
Machine learning in the detection and management of atrial fibrillation
FK Wegner, L Plagwitz, F Doldi, C Ellermann… - Clinical Research in …, 2022 - Springer
Abstract Machine learning has immense novel but also disruptive potential for medicine.
Numerous applications have already been suggested and evaluated concerning …
Numerous applications have already been suggested and evaluated concerning …
Mobile health technology in atrial fibrillation
Introduction Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming
widespread, thanks to everyday life devices, such as smartphones. Their use is validated …
widespread, thanks to everyday life devices, such as smartphones. Their use is validated …
Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis
Cardiovascular diseases are one of the leading global causes of mortality. Currently,
clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) …
clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) …
Wearables in cardiovascular disease
Wearable devices stand to revolutionize the way healthcare is delivered. From consumer
devices that provide general health information and screen for medical conditions to medical …
devices that provide general health information and screen for medical conditions to medical …
Different ventricular fibrillation types in low-dimensional latent spaces
The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed
that different mechanisms might exist. Moreover, conventional analysis methods do not …
that different mechanisms might exist. Moreover, conventional analysis methods do not …
A training pipeline of an arrhythmia classifier for atrial fibrillation detection using Photoplethysmography signal
Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection
for its convenience in use and similarity in physiological origin to electrocardiogram (ECG) …
for its convenience in use and similarity in physiological origin to electrocardiogram (ECG) …