211 research outputs found
Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG
Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%
A Multitier Deep Learning Model for Arrhythmia Detection
Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary
causes of death globally. The 12-lead ECG is a cheap and commonly accessible
tool to identify cardiac abnormalities. Early and accurate diagnosis will allow
early treatment and intervention to prevent severe complications of
cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge
2020, our objective is to develop an algorithm that automatically identifies 27
ECG abnormalities from 12-lead ECG recordings
Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet
Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is
clinically identified with irregular and rapid heartbeat rhythm. AF puts a
patient at risk of forming blood clots, which can eventually lead to heart
failure, stroke, or even sudden death. It is of critical importance to develop
an advanced analytical model that can effectively interpret the
electrocardiography (ECG) signals and provide decision support for accurate AF
diagnostics. In this paper, we propose an innovative deep-learning method for
automated AF identification from single-lead ECGs. We first engage the
continuous wavelet transform (CWT) to extract time-frequency features from ECG
signals. Then, we develop a convolutional neural network (CNN) structure that
incorporates ResNet for effective network training and multi-branching
architectures for addressing the imbalanced data issue to process the 2D
time-frequency features for AF classification. We evaluate the proposed
methodology using two real-world ECG databases. The experimental results show a
superior performance of our method compared with traditional deep learning
models
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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