AIMC Topic: Arrhythmias, Cardiac

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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...
Arrhythmias, Cardiac Neural Networks, Computer Algorithms Electrocardiography Signal Processing, Computer-Assisted Humans
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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...
Arrhythmias, Cardiac Electrocardiography Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans
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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...
Machine Learning Signal Processing, Computer-Assisted Humans Signal-To-Noise Ratio Tachycardia, Ventricular Electrocardiography, Ambulatory Arrhythmias, Cardiac
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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.
Humans Defibrillators, Implantable Wearable Electronic Devices Arrhythmias, Cardiac Artificial Intelligence Algorithms
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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...
Male Middle Aged Female Aged Humans Arrhythmias, Cardiac Machine Learning Tachycardia, Ventricular Retrospective Studies Electrocardiography Defibrillators, Implantable Cluster Analysis
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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...
Databases, Factual Electrocardiography Neural Networks, Computer Algorithms Humans Signal Processing, Computer-Assisted Arrhythmias, Cardiac
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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, ...
Arrhythmias, Cardiac Electrocardiography Signal Processing, Computer-Assisted Humans Deep Learning
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The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias.

The Journal of physiology Sep 9, 2024
Risk Assessment Deep Learning Arrhythmias, Cardiac Humans
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A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

Scientific reports Sep 6, 2024
The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essenti...
Humans Signal Processing, Computer-Assisted Arrhythmias, Cardiac Deep Learning Neural Networks, Computer Electrocardiography Algorithms
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ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

Computers in biology and medicine Aug 27, 2024
We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the...
Signal Processing, Computer-Assisted Deep Learning Heart Rate Humans Arrhythmias, Cardiac Electrocardiography Databases, Factual
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