AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Arrhythmias, Cardiac

Showing 191 to 200 of 272 articles

Clear Filters

ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.

Physiological measurement
OBJECTIVE: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the gen...

Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks.

IEEE journal of biomedical and health informatics
This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points ...

Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.

Computers in biology and medicine
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewe...

Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion.

IEEE transactions on bio-medical engineering
OBJECTIVE: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenari...

A novel application of deep learning for single-lead ECG classification.

Computers in biology and medicine
Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram ...

Experimental validation of robot-assisted cardiovascular catheterization: model-based versus model-free control.

International journal of computer assisted radiology and surgery
PURPOSE: In cardiac electrophysiology, a long and flexible catheter is delivered to a cardiac chamber for the treatment of arrhythmias. Although several robot-assisted platforms have been commercialized, the disorientation in tele-operation is still ...

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

Computers in biology and medicine
Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations o...

A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length.

Physiological measurement
OBJECTIVE: Atrial fibrillation (AF) is a major cause of hospitalization and death in the United States. Moreover, as the average age of individuals increases around the world, early detection and diagnosis of AF become even more pressing. In this pap...