[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 …
Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting
electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis …
electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis …
Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review
G Al Hinai, S Jammoul, Z Vajihi… - European Heart Journal …, 2021 - academic.oup.com
The aim of this review was to assess the evidence for deep learning (DL) analysis of resting
electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular …
electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular …
[图书][B] Goldberger's Clinical Electrocardiography-A Simplified Approach: First South Asia Edition-E-Book
AL Goldberger, ZD Goldberger, A Shvilkin - 2017 - books.google.com
Ideal for students and as a review for practicing clinicians, Goldberger's Clinical
Electrocardiography explains the fundamentals of ECG interpretation and analysis, helping …
Electrocardiography explains the fundamentals of ECG interpretation and analysis, helping …
Effectively modeling time series with simple discrete state spaces
Time series modeling is a well-established problem, which often requires that methods (1)
expressively represent complicated dependencies,(2) forecast long horizons, and (3) …
expressively represent complicated dependencies,(2) forecast long horizons, and (3) …
Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment
This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia
classification from ECG signal inputs. ArrhyMon targets to detect and classify six different …
classification from ECG signal inputs. ArrhyMon targets to detect and classify six different …
Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
S Gustafsson, D Gedon, E Lampa, AH Ribeiro… - Scientific Reports, 2022 - nature.com
Myocardial infarction diagnosis is a common challenge in the emergency department. In
managed settings, deep learning-based models and especially convolutional deep models …
managed settings, deep learning-based models and especially convolutional deep models …
Ecg-fm: An open electrocardiogram foundation model
K McKeen, L Oliva, S Masood, A Toma, B Rubin… - arXiv preprint arXiv …, 2024 - arxiv.org
The electrocardiogram (ECG) is a ubiquitous diagnostic test. Conventional task-specific
ECG analysis models require large numbers of expensive ECG annotations or associated …
ECG analysis models require large numbers of expensive ECG annotations or associated …
Ecg classification using an optimal temporal convolutional network for remote health monitoring
Increased life expectancy in most countries is a result of continuous improvements at all
levels, starting from medicine and public health services, environmental and personal …
levels, starting from medicine and public health services, environmental and personal …
Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals
Heart diseases are leading to death across the globe. Exact detection and treatment for
heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one …
heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one …