AIMC Topic: Electrocardiography

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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Mathematical biosciences and engineering : MBE Mar 22, 2024
Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imba...
Neural Networks, Computer Algorithms Databases, Factual Humans Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Sensitivity and Specificity Deep Learning Reproducibility of Results
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Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

Mathematical biosciences and engineering : MBE Feb 26, 2024
The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based ...
Electric Power Supplies Neural Networks, Computer Signal Processing, Computer-Assisted Algorithms Electrocardiography Signal-To-Noise Ratio
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[Mental fatigue state recognition method based on convolution neural network and long short-term memory].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2024
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can...
Humans Algorithms Mental Fatigue Electrocardiography Neural Networks, Computer
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The rough but fascinating road to estimate peak exercise oxygen uptake by resting electrocardiogram-based deep learning.

European journal of preventive cardiology Jan 25, 2024
Humans Electrocardiography Oxygen Exercise Test Exercise Deep Learning
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Heart Rate and its Variability From Short-Term ECG Recordings as Potential Biomarkers for Detecting Mild Cognitive Impairment.

American journal of Alzheimer's disease and other dementias Jan 1, 2024
Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) reflect autonomic dysfunction associated with neurodegeneration making them biomarkers suitable for detecting Mild Cognitive Impairment (MCI). The study involves 297 urban Indian parti...
Heart Rate Biomarkers Female Cognitive Dysfunction Aged Male Humans India Middle Aged Electrocardiography Machine Learning
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An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Heart disease represents the leading cause of death globally. Timely diagnosis and treatment can prevent cardiovascular issues. An Electrocardiograms (ECG) serves as a diagnostic tool for identifying heart difficulties. Cardiovascular Dis...
Electrocardiography Neural Networks, Computer Cardiovascular Diseases Deep Learning Humans
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Mental fatigue recognition study based on 1D convolutional neural network and short-term ECG signals.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Mental fatigue has become a non-negligible health problem in modern life, as well as one of the important causes of social transportation, production and life accidents.
Humans Machine Learning Electrocardiography Neural Networks, Computer Mental Fatigue Adult Female Male
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Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis.

The Journal of toxicological sciences Jan 1, 2024
Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in...
Kainic Acid 4-Aminopyridine Ranolazine Bupropion Autonomic Nervous System Convulsants Heart Rate Telemetry Animals Male Biomarkers Dose-Response Relationship, Drug Machine Learning Seizures Electrocardiography
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Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays.

International heart journal Jan 1, 2024
Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aids for prognostication and treatment planning. While single-modality deep neural networks (DNNs) have shown promising performance for detecting cardiac...
Myocardial Ischemia X-Rays Deep Learning Electrocardiography Humans Risk Assessment
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Using artificial intelligence-enabled electrocardiogram to predict cardiac resynchronization therapy outcomes of left bundle branch area pacing.

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 Dec 28, 2023
Heart Failure Ventricular Function, Left Bundle of His Humans Cardiac Resynchronization Therapy Electrocardiography Cardiac Pacing, Artificial Heart Conduction System Artificial Intelligence Treatment Outcome
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