A cybertwin based multimodal network for ecg patterns monitoring using deep learning
In next-generation network architecture, the Cybertwin drove the sixth generation of cellular
networks sixth-generation (6G) to play an active role in many applications, such as …
networks sixth-generation (6G) to play an active role in many applications, such as …
Automated ECG classification based on 1D deep learning network
CY Chen, YT Lin, SJ Lee, WC Tsai, TC Huang, YH Liu… - Methods, 2022 - Elsevier
The standard 12-lead electrocardiogram (ECG) records the heart's electrical activity from
electrodes on the skin, and is widely used in screening and diagnosis of the cardiac …
electrodes on the skin, and is widely used in screening and diagnosis of the cardiac …
Long-term wearable electrocardiogram signal monitoring and analysis based on convolutional neural network
L Meng, K Ge, Y Song, D Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Wearable devices are increasingly popular for health monitoring via electrocardiograms
(ECGs) as they can portably monitor heart conditions over a long time. However, so far there …
(ECGs) as they can portably monitor heart conditions over a long time. However, so far there …
Patient-specific ECG classification by deeper CNN from generic to dedicated
This paper presents a new mechanism which is more effective for wearable devices to
classify patient-specific electrocardiogram (ECG) heartbeats. In our method, a Generic …
classify patient-specific electrocardiogram (ECG) heartbeats. In our method, a Generic …
Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals
Coronary artery disease (CAD) is the most common cause of heart disease globally. This is
because there is no symptom exhibited in its initial phase until the disease progresses to an …
because there is no symptom exhibited in its initial phase until the disease progresses to an …
The comparison of different feed forward neural network architectures for ECG signal diagnosis
HG Hosseini, D Luo, KJ Reynolds - Medical engineering & physics, 2006 - Elsevier
The electrocardiograms (ECGs) record the electrical activity of the heart and are used to
diagnose many heart disorders. This paper proposes a two-stage feed forward neural …
diagnose many heart disorders. This paper proposes a two-stage feed forward neural …
Homecare-oriented ECG diagnosis with large-scale deep neural network for continuous monitoring on embedded devices
S Ran, X Yang, M Liu, Y Zhang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The accurate electrocardiogram (ECG) interpretation is important for several potentially life-
threatening cardiac diseases. Recently developed deep learning methods show their ability …
threatening cardiac diseases. Recently developed deep learning methods show their ability …
Real-time patient-specific ECG classification by 1-D convolutional neural networks
Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG)
classification and monitoring system. Methods: An adaptive implementation of 1-D …
classification and monitoring system. Methods: An adaptive implementation of 1-D …
An effective LSTM recurrent network to detect arrhythmia on imbalanced ECG dataset
J Gao, H Zhang, P Lu, Z Wang - Journal of healthcare …, 2019 - Wiley Online Library
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram
(ECG) beat plays a significant role in computer‐aided arrhythmia diagnosis systems …
(ECG) beat plays a significant role in computer‐aided arrhythmia diagnosis systems …
[HTML][HTML] Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures
AS Eltrass, MB Tayel, AI Ammar - Neural Computing and Applications, 2022 - Springer
Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
types of heart disorders. In this study, a novel hybrid approach of deep neural network …