AIMC Topic: Electrocardiography

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Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning.

IEEE journal of biomedical and health informatics
Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification,...

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.

Computers in biology and medicine
A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to t...

Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

Computers in biology and medicine
The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (ele...

Enhancing clinical decision support with physiological waveforms - A multimodal benchmark in emergency care.

Computers in biology and medicine
BACKGROUND: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including r...

Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.

BMC cardiovascular disorders
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...

Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection.

Computer methods in biomechanics and biomedical engineering
Fetal electrocardiogram (ECG) provides a non-invasive means to assess fetal heart health, but isolating the fetal signal from the dominant maternal ECG remains challenging. This study introduces the FHH-AMGNN-HTSOA-ECG-AD method for enhanced fetal ar...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BMC pediatrics
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...

SEISMIC-HF 1: key findings from AHA24 and implications for remote cardiac monitoring.

Heart failure reviews
While there is continued progress in developing therapies for patients with heart failure, the condition results in significant morbidity and a sizeable economic impact on our society. Recent advances in wearable sensors combined with machine learnin...

FADE: Forecasting for anomaly detection on ECG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multip...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...