Advances of ecg sensors from hardware, software and format interoperability perspectives
It is well-known that cardiovascular disease is one of the major causes of death worldwide
nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by …
nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by …
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 …
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 …
Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG
Atrial fibrillation (Afib) is a heart arrhythmia that is linked to a number of other cardiac-related
issues. The incidence of Afib increases with age, causing high risks of stroke. Accurate and …
issues. The incidence of Afib increases with age, causing high risks of stroke. Accurate and …
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 …
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 …
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 …
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 …
[HTML][HTML] A deep-learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: algorithm development
Background The detection of dyskalemias—hypokalemia and hyperkalemia—currently
depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia …
depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia …