AIMC Topic: Electrocardiography

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[Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To enhance the accuracy and reliability of 12-lead electrocardiogram (ECG) automatic diagnosis. METHODS: Herein we propose a 12-lead ECG automatic diagnosis model based on deep feature fusion (MRHL-ECGNet), which consists of a multi-scale...

The Hypno-PC: uncovering sleep dynamics through principal component analysis and hidden Markov modeling of electrophysiological signals.

Sleep
Manual sleep scoring segments sleep into discrete 30-s epochs (wake, non-rapid-eye-movement [NREM] 1-3, rapid-eye-movement [REM]), yet substantial evidence suggests that sleep unfolds as a continuous, microstate-rich process. Using a data-driven appr...

[Automatic detection and visualization of myocardial infarction in electrocardiograms based on an interpretable deep learning model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Automated detection of myocardial infarction (MI) is crucial for preventing sudden cardiac death and enabling early intervention in cardiovascular diseases. This paper proposes a deep learning framework based on a lightweight convolutional neural net...

Artificial intelligence-enhanced electrocardiogram diastolic function grade predicts post-septal myectomy mortality in hypertrophic cardiomyopathy.

The Journal of thoracic and cardiovascular surgery
BACKGROUNDS: Diastolic dysfunction is an important pathophysiologic feature of hypertrophic cardiomyopathy that is often challenging to determine noninvasively. This study investigated whether a novel artificial intelligence-enabled electrocardiograp...

Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography.

IEEE transactions on bio-medical engineering
OBJECTIVE: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized computed tomography angiography gating (CTA).

ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction.

IEEE transactions on bio-medical engineering
OBJECTIVE: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries requiring immediate refe...

Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study.

European heart journal
BACKGROUND AND AIMS: Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to d...

Incorporating respiratory signals for machine learning-based multimodal sleep stage classification: a large-scale benchmark study with actigraphy and heart rate variability.

Sleep
Insufficient sleep quality is directly linked to various diseases, making reliable sleep monitoring crucial for prevention, diagnosis, and treatment. As sleep laboratories are cost- and resource-prohibitive, wearable sensors offer a promising alterna...

ECG Synthesis and Utility Analysis - A Diffusion Model Based Approach.

Studies in health technology and informatics
INTRODUCTION: With the growing demand for privacy-preserving healthcare solutions, the generation of synthetic electrocardiograms (ECGs) offers a valuable alternative to using real patient data.