AIMC Topic: Arrhythmias, Cardiac

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An arrhythmia classification using a deep learning and optimisation-based methodology.

Journal of medical engineering & technology Feb 14, 2025
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals ar...
Arrhythmias, Cardiac Humans Wavelet Analysis Algorithms Deep Learning Electrocardiography Signal Processing, Computer-Assisted
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Active learning and margin strategies for arrhythmia classification in implantable devices.

Computers in biology and medicine Feb 13, 2025
BACKGROUND AND OBJECTIVES: The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowle...
Supervised Machine Learning Arrhythmias, Cardiac Defibrillators, Implantable Machine Learning Algorithms Humans
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Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

PloS one Feb 12, 2025
Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in ...
Humans Arrhythmias, Cardiac Atrial Fibrillation Heart Rate Photoplethysmography Electrocardiography Signal Processing, Computer-Assisted Neural Networks, Computer Wearable Electronic Devices
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Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

Scientific reports Feb 11, 2025
The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking th...
Deep Learning Electrocardiography Signal Processing, Computer-Assisted Humans Algorithms Animals Models, Cardiovascular Astacoidea Arrhythmias, Cardiac Finite Element Analysis
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AI Accelerator With Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the diagnostic process for cardiologists. The proposed algorithm includes a naive preprocessing procedure for electrocardiography (ECG) data applicable to...
Humans Neural Networks, Computer Electrocardiography Artificial Intelligence Signal Processing, Computer-Assisted Algorithms Arrhythmias, Cardiac Databases, Factual
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Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Nature medicine Feb 10, 2025
Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting ...
Arrhythmias, Cardiac Middle Aged Artificial Intelligence Electrocardiography, Ambulatory Humans Female Algorithms Aged Physicians Male
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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion.

Sensors (Basel, Switzerland) Jan 23, 2025
Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management ...
Algorithms Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Neural Networks, Computer
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Application of machine learning in predicting postoperative arrhythmia following transcatheter closure of perimembranous ventricular septal defects.

Kardiologia polska Jan 2, 2025
BACKGROUND: Arrhythmia is a frequent complication following transcatheter device closure of perimembranous ventricular septal defects (pmVSD). However, there is currently a lack of a convenient tool for predicting postoperative arrhythmia.
Child Arrhythmias, Cardiac Cardiac Catheterization Heart Septal Defects, Ventricular Postoperative Complications Infant Child, Preschool Female Male Humans Machine Learning Retrospective Studies
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Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Sensors (Basel, Switzerland) Dec 19, 2024
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, ...
Bayes Theorem Humans Signal Processing, Computer-Assisted Neural Networks, Computer Databases, Factual Arrhythmias, Cardiac Algorithms Electrocardiography
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Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.

Sensors (Basel, Switzerland) Dec 17, 2024
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbea...
Databases, Factual Wavelet Analysis Deep Learning Signal Processing, Computer-Assisted Electrocardiography Humans Arrhythmias, Cardiac Neural Networks, Computer Algorithms
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