AIMC Topic: Arrhythmias, Cardiac

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A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Nature communications Jul 9, 2021
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a v...
Humans Time Factors ROC Curve Clinical Decision-Making Decision Support Systems, Clinical Arrhythmias, Cardiac Deep Learning Datasets as Topic Models, Cardiovascular Electrocardiography Seasons
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The Role of Artificial Intelligence in Arrhythmia Monitoring.

Cardiac electrophysiology clinics Jul 8, 2021
Arrhythmia management has been revolutionized by the ability to monitor the cardiac rhythm in a patient's home environment in real-time using high-fidelity prescription-grade and commercially available wearable electrodes. The vast amount of digitall...
Machine Learning Electrocardiography Humans Arrhythmias, Cardiac Monitoring, Physiologic Artificial Intelligence
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Detection and classification of arrhythmia using an explainable deep learning model.

Journal of electrocardiology Jun 26, 2021
BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been crit...
Retrospective Studies Deep Learning Electrocardiography Humans Algorithms Arrhythmias, Cardiac
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Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks.

Biosensors Jun 8, 2021
Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient's condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians wi...
Neural Networks, Computer Algorithms Electrocardiography Humans Arrhythmias, Cardiac Internet of Things
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ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine Apr 30, 2021
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...
Models, Cardiovascular Computational Biology Signal-To-Noise Ratio Algorithms Arrhythmias, Cardiac Wavelet Analysis Electrocardiography Humans Databases, Factual Heart Rate Neural Networks, Computer Signal Processing, Computer-Assisted
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Automated ECG classification based on 1D deep learning network.

Methods (San Diego, Calif.) Apr 27, 2021
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 conditions due to its low price and non-invasive characteristics. Manual ex...
Electrodes Arrhythmias, Cardiac Algorithms Deep Learning Electrocardiography Humans Neural Networks, Computer
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KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Journal of healthcare engineering Apr 24, 2021
Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep ne...
Humans Computers, Handheld Neural Networks, Computer Signal Processing, Computer-Assisted Algorithms Electrocardiography Arrhythmias, Cardiac
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Interpretable heartbeat classification using local model-agnostic explanations on ECGs.

Computers in biology and medicine Apr 16, 2021
Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG interpretation is subjective and prone to errors. Machine learning models are often developed and ...
Heart Rate Electrocardiography Arrhythmias, Cardiac Artificial Intelligence Machine Learning Humans
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Comparing performance of iterative and non-iterative algorithms on various feature schemes for arrhythmia analysis.

Methods (San Diego, Calif.) Apr 9, 2021
To evaluate the performance of the classic machine learning algorithms and the effectiveness of various features, the iterative algorithms (i.e., support vector machine (SVM), and least-squares SVM (LS-SVM)) and non-iterative algorithms (i.e., random...
Machine Learning Algorithms Bayes Theorem Arrhythmias, Cardiac Support Vector Machine Humans
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A New ECG Denoising Framework Using Generative Adversarial Network.

IEEE/ACM transactions on computational biology and bioinformatics Apr 6, 2021
This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks...
Algorithms Databases, Factual Signal Processing, Computer-Assisted Arrhythmias, Cardiac Neural Networks, Computer Electrocardiography Humans Machine Learning
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