AIMC Topic: Arrhythmias, Cardiac

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Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks.

Scientific reports May 12, 2025
Detecting and classifying arrhythmias is essential in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features...
Convolutional Neural Networks Electrocardiography Arrhythmias, Cardiac Databases, Factual Deep Learning Humans Neural Networks, Computer Algorithms
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Are Wearable ECG Devices Ready for Hospital at Home Application?

Sensors (Basel, Switzerland) May 9, 2025
The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the r...
Humans Hospitals Wearable Electronic Devices Telemedicine Atrial Fibrillation Home Care Services Electrocardiography Monitoring, Physiologic Arrhythmias, Cardiac
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State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology May 7, 2025
AIMS: Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and underst...
Consensus Arrhythmias, Cardiac Atrial Fibrillation Humans Artificial Intelligence Delphi Technique Electrophysiologic Techniques, Cardiac Cardiac Electrophysiology Checklist Death, Sudden, Cardiac
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Deep learning for electrocardiogram interpretation: Bench to bedside.

European journal of clinical investigation Apr 1, 2025
BACKGROUND: Recent advancements in deep learning (DL), a subset of artificial intelligence, have shown the potential to automate and improve disease recognition, phenotyping and prediction of disease onset and outcomes by analysing various sources of...
Randomized Controlled Trials as Topic Arrhythmias, Cardiac Cardiovascular Diseases Deep Learning Electrocardiography Humans Algorithms
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[Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2025
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia feature...
Algorithms Deep Learning Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Neural Networks, Computer Classification Algorithms
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[The joint analysis of heart health and mental health based on continual learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2025
Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are cons...
Mental Disorders Mental Health Neural Networks, Computer Cardiovascular Diseases Machine Learning Electrocardiography Humans Algorithms Signal Processing, Computer-Assisted Arrhythmias, Cardiac
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Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.

PloS one Jan 1, 2025
Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. An electrocardiogram (ECG) is a non-invasive and quicker method for diagnosing abnormal heart conditions. While research has extensively focused on EC...
Arrhythmias, Cardiac Wavelet Analysis Neural Networks, Computer Cardiovascular Diseases Deep Learning Humans Electrocardiography Signal Processing, Computer-Assisted
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LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

Physiological reports Sep 1, 2024
The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and re...
Arrhythmias, Cardiac Electrocardiography Neural Networks, Computer Signal Processing, Computer-Assisted Algorithms Humans Deep Learning
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[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Aug 25, 2024
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early ...
Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Sensitivity and Specificity Humans Deep Learning Heart Rate Neural Networks, Computer Death, Sudden, Cardiac Algorithms
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ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Electrocardiogram data provide a tremendous opportunity for the detection of various types of cardiac arrhythmia. Recent advancement in ubiquitous wearable devices with incorporated ECG sensors offers an opportunity for a real-time monitoring system ...
Arrhythmias, Cardiac Electrocardiography Humans Atrial Fibrillation Signal Processing, Computer-Assisted Algorithms Supervised Machine Learning
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