AIMC Topic: Electrocardiography

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Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.

Scientific reports
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...

Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.

Current atherosclerosis reports
PURPOSE OF REVIEW: In this paper we review recent advancements in the diagnosis and management of atrial fibrillation through machine learning (ML).

Current State of Artificial Intelligence in Assessing Cardiac Function.

Current cardiology reports
PURPOSE OF REVIEW: Accurate, timely quantification of cardiac function is central to the diagnosis, management, and monitoring of cardiovascular disease. This review synthesizes recent advances in artificial intelligence (AI) applications across the ...

Enhancing cardiac disease prediction with explainable bidirectional LSTM.

Scientific reports
Cardiovascular disorders (heart diseases) are the most prevalent cause of death on a global scale. So early detection and classification increase the likelihood of survival. In the context of machine learning techniques, there is always a need for an...

A multimodal physiological dataset for non-invasive blood glucose estimation.

Scientific data
Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs)...

TF-crossnet: a cross-modal attention fusion network for cardiovascular disease classification using pcg and ecg signals.

Biomedical physics & engineering express
Electrocardiogram (ECG) and phonocardiogram (PCG) have emerged as crucial non-invasive and portable diagnostic modalities for early cardiovascular disease (CVD) screening. Despite the individual merits of these signal modalities in CVD detection, sig...

Deep Learning-Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation.

Circulation
BACKGROUND: Drug-induced QT prolongation after successful inpatient loading of class III antiarrhythmics may occur during routine outpatient care. Insertable cardiac monitors offer continuous signals but are limited by single-lead configuration. We h...

Classification of cardiac electrical signals between patients with myocardial infarction and healthy controls by using time-frequency features and 3D convolutional neural networks.

Biomedical physics & engineering express
Electrocardiogram (ECG) signal classification plays an important role in myocardial infarction (MI) detection and screening. Despite that much progress has been made, the interpretation of ECG signals is still extremely time-consuming, and heavily re...

Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.

BMC medical informatics and decision making
BACKGROUND: Myocardial infarction (MI) is a life-threatening condition caused by sudden interruption of blood supply to the heart. Electrocardiogram (ECG) is the primary tool for MI diagnosis, but interpretation challenges exist. This study aimed to ...

AI-based approach for heart failure readmission prediction using SCG, ECG, and GSR signals.

Physiological measurement
Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization, and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by pred...