AIMC Topic: Arrhythmias, Cardiac
Clear Filters Showing 31 to 40 of 287 articles
Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.
Circulation. Arrhythmia and electrophysiology
Dec 16, 2024
BACKGROUND: Large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT) excel at interpreting unstructured data from public sources, yet are limited when responding to queries on private repositories, such as electronic hea...
An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.
IEEE transactions on biomedical circuits and systems
Dec 9, 2024
This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neural network accelerator, achieved through algorithm-hardware co-design to optimize hardware resource...
Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System.
Medical engineering & physics
Nov 29, 2024
Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals is challenging. However, long-duration ECG data are susceptible to various types of noise during acquisition. To tackle the problem, Subspace Search ...
A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.
Sensors (Basel, Switzerland)
Nov 11, 2024
Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCNNs is chal...
Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.
Technology and health care : official journal of the European Society for Engineering and Medicine
Nov 10, 2024
BackgroundDeep neural networks (DNNs) have recently been significantly applied to automatic arrhythmia classification. However, their classification accuracy still has room for improvement.ObjectivesThe aim of this study is to address the existing li...
Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach.
Computers in biology and medicine
Oct 23, 2024
Noise detection in ambulatory electrocardiography is investigated as a machine learning binary classification problem on a set of twelve noise indices. Ten of these noise indices are replicated from relevant scientific literature. Two novel noise ind...
Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.
Current cardiology reports
Oct 18, 2024
PURPOSE OF REVIEW: To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias.
Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.
Circulation journal : official journal of the Japanese Circulation Society
Oct 1, 2024
BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however...
An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm.
Sensors (Basel, Switzerland)
Sep 29, 2024
Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention...
3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection.
Computers in biology and medicine
Sep 9, 2024
Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, ...
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