A global and updatable ECG beat classification system based on recurrent neural networks and active learning
The key challenges faced in the automatic diagnosis of arrhythmia by electrocardiogram
(ECG) is enormous differences among individual patients and high cost of labeling clinical …
(ECG) is enormous differences among individual patients and high cost of labeling clinical …
Region aggregation network: improving convolutional neural network for ECG characteristic detection
Detection of ECG characteristic points serves as the first step in automated ECG analysis
techniques. We propose a novel end-to-end deep learning scheme called Region …
techniques. We propose a novel end-to-end deep learning scheme called Region …
Classification of ECG arrhythmia with machine learning techniques
HI Bulbul, N Usta, M Yildiz - 2017 16th IEEE International …, 2017 - ieeexplore.ieee.org
The ECG uses some methods to diagnose these cardiac arrhythmias and tries to correct the
diagnosis. ECG signals are characterized by a collection of waves such as P, Q, R, S, T …
diagnosis. ECG signals are characterized by a collection of waves such as P, Q, R, S, T …
Intelligent Gateway Based Human Cardiopulmonary Health Monitoring System
Y Zhao, Y Liu, H Zhou, Y Wei, Y Yu, S Lu… - Journal of …, 2023 - Wiley Online Library
Cardiopulmonary diseases, including cardiovascular disease (CVD) and chronic obstructive
pulmonary disorder (COPD), are prevalent in the elderly population. Early identification, long …
pulmonary disorder (COPD), are prevalent in the elderly population. Early identification, long …
Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities
Objective Abnormalities in impulse propagation and cardiac repolarization are frequent in
hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms …
hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms …
Real-time ECG delineation with randomly selected wavelet transform feature and random walk estimation
Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic
information about heart diseases. We propose a novel feature extraction and machine …
information about heart diseases. We propose a novel feature extraction and machine …
ECG delineation using a piecewise Gaussian derivative model with parameters estimated from scale-dependent algebraic expressions
Automatic methods for the detection of characteristic points in electrocardiography signals
support cardiologists in assessing the state of a patient's cardiovascular system. In this work …
support cardiologists in assessing the state of a patient's cardiovascular system. In this work …
Improving automatic detection of ECG abnormality with less manual annotations using Siamese network
F Yang, G Wang, C Luo, Z Ding - 2021 43rd Annual …, 2021 - ieeexplore.ieee.org
Electrocardiography is a very common, non-invasive diagnostic procedure and its
interpretation is increasingly supported by automatic interpretation algorithms. Recently …
interpretation is increasingly supported by automatic interpretation algorithms. Recently …
An electrocardiogram delineator via deep segmentation network
D Jia, W Zhao, Z Li, J Hu, C Yan… - 2019 41st Annual …, 2019 - ieeexplore.ieee.org
Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points,
which contain critical diagnostic information about cardiac diseases. We treat the ECG …
which contain critical diagnostic information about cardiac diseases. We treat the ECG …
An ensemble neural network for multi-label classification of electrocardiogram
An electrocardiogram (ECG) record potentially contains multiple abnormalities concurrently,
therefore multi-label classification of ECG is significant in clinical scenarios. In this paper, we …
therefore multi-label classification of ECG is significant in clinical scenarios. In this paper, we …