AI Medical Compendium Topic

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Electrocardiography

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Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.

Current cardiology reports
PURPOSE OF REVIEW: Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovas...

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

Aging
BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aim...

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

Pacing and clinical electrophysiology : PACE
BACKGROUND: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare d...

A lightweight deep learning approach for detecting electrocardiographic lead misplacement.

Physiological measurement
. Electrocardiographic (ECG) lead misplacement can result in distorted waveforms and amplitudes, significantly impacting accurate interpretation. Although lead misplacement is a relatively low-probability event, with an incidence ranging from 0.4% to...

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

PloS one
Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcar...

ECG waveform generation from radar signals: A deep learning perspective.

Computers in biology and medicine
Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended ele...

Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning.

Journal of the American Heart Association
BACKGROUND: Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug-induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and ...

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach.

Biomedical physics & engineering express
Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an ...