AIMC Topic: Electrocardiography

Clear Filters Showing 591 to 600 of 1388 articles

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Computers in biology and medicine
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure...

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Biosensors
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel...

A deep learning approach identifies new ECG features in congenital long QT syndrome.

BMC medicine
BACKGROUND: Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. I...

Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.

Sensors (Basel, Switzerland)
The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this rese...

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis.

Computational intelligence and neuroscience
Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achiev...

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Sensors (Basel, Switzerland)
Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-qualit...

Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals.

Computers in biology and medicine
Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI, accurate electrocardiography (ECG) is important for timely diagnosis and intervention in the emergency setting. Machine learning is increasingly being explored for...

Research on recognition and classification of pulse signal features based on EPNCC.

Scientific reports
To rapidly obtain the complete characterization information of pulse signals and to verify the sensitivity and validity of pulse signals in the clinical diagnosis of related diseases. In this paper, an improved PNCC method is proposed as a supplement...

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing.

IEEE journal of biomedical and health informatics
Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable counterparts that are more familiar to healthcare providers. For instance, bio-impedance (Bio-Z) is...

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

International urology and nephrology
PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically do...