State‐of‐the‐art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system
With the digitization of all records and processes, and prevalence of cloud-driven services
and Internet of Things, today's era can truly be considered as an era of data. Machine …
and Internet of Things, today's era can truly be considered as an era of data. Machine …
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
Survey on atrial fibrillation detection from a single-lead ECG wave for Internet of Medical Things
Y Liu, J Chen, N Bao, BB Gupta, Z Lv - Computer Communications, 2021 - Elsevier
Recent advances of Internet of Medical Things have allowed for continuous heart rhythm
monitoring in a comfortable fashion. Single lead Electrocardiograph (ECG) is first collected …
monitoring in a comfortable fashion. Single lead Electrocardiograph (ECG) is first collected …
Explainable prediction of acute myocardial infarction using machine learning and shapley values
The early and accurate detection of the onset of acute myocardial infarction (AMI) is
imperative for the timely provision of medical intervention and the reduction of its mortality …
imperative for the timely provision of medical intervention and the reduction of its mortality …
ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network
Objective: The electrocardiogram (ECG) provides an effective, non-invasive approach for
clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the …
clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the …
[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 …
A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation
P Cao, X Li, K Mao, F Lu, G Ning, L Fang… - … Signal Processing and …, 2020 - Elsevier
Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) recordings
remains challenging in real clinical settings. Deep neural networks (DNN) emerge as a …
remains challenging in real clinical settings. Deep neural networks (DNN) emerge as a …
Atrial fibrillation detection using a feedforward neural network
Purpose In this study, we aimed to develop an automatic atrial fibrillation detection
technique for the early prediction of atrial fibrillation, that can be used with wearable devices …
technique for the early prediction of atrial fibrillation, that can be used with wearable devices …
Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings
Background and objective Atrial fibrillation (AF) is the most common form of cardiac rhythm
disorder. Early detection of AF can result in a lower risk of stroke, heart failure, systemic …
disorder. Early detection of AF can result in a lower risk of stroke, heart failure, systemic …
Sequence to sequence ECG cardiac rhythm classification using convolutional recurrent neural networks
T Pokaprakarn, RR Kitzmiller… - IEEE journal of …, 2021 - ieeexplore.ieee.org
This paper proposes a novel deep learning architecture involving combinations of
Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers …
Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers …