[HTML][HTML] Comprehensive survey of computational ECG analysis: Databases, methods and applications
Electrocardiogram (ECG) recordings are indicative for the state of the human heart.
Automatic analysis of these recordings can be performed using various computational …
Automatic analysis of these recordings can be performed using various computational …
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and
diagnose several abnormal arrhythmias. While there have been remarkable improvements …
diagnose several abnormal arrhythmias. While there have been remarkable improvements …
[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of
many people around the world and is associated with a five-fold increased risk of stroke and …
many people around the world and is associated with a five-fold increased risk of stroke and …
Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress
Continuous wearable sensor data in high resolution contain physiological and behavioral
information that can be utilized to predict human health and wellbeing, establishing the …
information that can be utilized to predict human health and wellbeing, establishing the …
Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis
Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous
in the daily practice of medicine largely due to several crucial unmet needs of healthcare …
in the daily practice of medicine largely due to several crucial unmet needs of healthcare …
Multi-label correlation guided feature fusion network for abnormal ECG diagnosis
Electrocardiographic (ECG) abnormalities are the most intuitive manifestation in the clinical
diagnosis of cardiovascular disease. Although significant progress has been achieved in …
diagnosis of cardiovascular disease. Although significant progress has been achieved in …
Transformers in biosignal analysis: A review
Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …
Through outstanding performance in natural language processing and superior capability to …