[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
[HTML][HTML] Comprehensive survey of computational ECG analysis: Databases, methods and applications
E Merdjanovska, A Rashkovska - Expert Systems with Applications, 2022 - Elsevier
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 …
Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
Deep neural network-estimated electrocardiographic age as a mortality predictor
EM Lima, AH Ribeiro, GMM Paixão, MH Ribeiro… - Nature …, 2021 - nature.com
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of
cardiovascular diseases. Here we propose that the age predicted by artificial intelligence …
cardiovascular diseases. Here we propose that the age predicted by artificial intelligence …
[HTML][HTML] Self-supervised representation learning from 12-lead ECG data
T Mehari, N Strodthoff - Computers in biology and medicine, 2022 - Elsevier
Abstract Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
J Lai, H Tan, J Wang, L Ji, J Guo, B Han, Y Shi… - Nature …, 2023 - nature.com
Cardiovascular disease is a major global public health problem, and intelligent diagnostic
approaches play an increasingly important role in the analysis of electrocardiograms …
approaches play an increasingly important role in the analysis of electrocardiograms …
ECG signal classification using deep learning techniques based on the PTB-XL dataset
The analysis and processing of ECG signals are a key approach in the diagnosis of
cardiovascular diseases. The main field of work in this area is classification, which is …
cardiovascular diseases. The main field of work in this area is classification, which is …
Automated ECG classification using a non-local convolutional block attention module
J Wang, X Qiao, C Liu, X Wang, YY Liu, L Yao… - Computer Methods and …, 2021 - Elsevier
Background and objective: Recent advances in deep learning have been applied to ECG
detection and obtained great success. The spatial and temporal information from ECG …
detection and obtained great success. The spatial and temporal information from ECG …
[HTML][HTML] Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for
cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the …
cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the …
Explainable AI decision model for ECG data of cardiac disorders
Electrocardiogram (ECG) data is used to monitor the electrical activity of the heart. It is
known that ECG data could help in detecting cardiac (heart) abnormalities. AI-enabled …
known that ECG data could help in detecting cardiac (heart) abnormalities. AI-enabled …