WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms
Background and objective Automated detection of arrhythmias from electrocardiograms
(ECGs) can be of considerable assistance to medical professionals in providing efficient …
(ECGs) can be of considerable assistance to medical professionals in providing efficient …
Automated inter-patient arrhythmia classification with dual attention neural network
H Lyu, X Li, J Zhang, C Zhou, X Tang, F Xu… - Computer Methods and …, 2023 - Elsevier
Background and objectives Arrhythmia classification based on electrocardiograms (ECG)
can enhance clinical diagnostic efficiency. However, due to the significant differences in the …
can enhance clinical diagnostic efficiency. However, due to the significant differences in the …
Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram
K Mallikarjunamallu, K Syed - PeerJ Computer Science, 2024 - peerj.com
Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable
electrocardiogram (ECG) monitors have been used for decades to monitor patients with …
electrocardiogram (ECG) monitors have been used for decades to monitor patients with …
A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer
The scarcity of annotated data is a common issue in the realm of heartbeat classification
based on deep learning. Transfer learning (TL) has emerged as an effective strategy for …
based on deep learning. Transfer learning (TL) has emerged as an effective strategy for …
Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence …
C Zhou, X Li, F Feng, J Zhang, H Lyu, W Wu… - Frontiers in …, 2023 - frontiersin.org
Objective: The objective of this research is to construct a method to alleviate the problem of
sample imbalance in classification, especially for arrhythmia classification. This approach …
sample imbalance in classification, especially for arrhythmia classification. This approach …
Refined self-attention transformer model for ECG-based arrhythmia detection
Y Tao, B Xu, Y Zhang - IEEE Transactions on Instrumentation …, 2024 - ieeexplore.ieee.org
As the length of electrocardiogram (ECG) sequences increases, most current transformer
models demand substantial computational resources for ECG arrhythmia detection …
models demand substantial computational resources for ECG arrhythmia detection …
Single and Multi-Lead ECG Heartbeat Classification Using XGBoost on Pre-trained CNN Feature Layer
NK Kancharla, V Brindha - 2024 - researchsquare.com
Electrocardiogram (ECG) is a common tool used in technology-assisted systems to help spot
heart rhythm problems. Doctors rely on it to identify and understand any irregularities in the …
heart rhythm problems. Doctors rely on it to identify and understand any irregularities in the …
A Kohonen Self Organizing Map (KSOM) Technique for Classification of Electrocardiogram (ECG) Signals
RS Babatunde, A Oyeranmi - LAUTECH JOURNAL OF …, 2024 - laujci.lautech.edu.ng
Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases.
Handling noisy ECG data, which is common in real-world situations makes accurate …
Handling noisy ECG data, which is common in real-world situations makes accurate …