AIMC Topic: Electrocardiography

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Deep Learning Using Electrocardiograms in Patients on Maintenance Dialysis.

Advances in kidney disease and health
Cardiovascular morbidity and mortality occur with an extraordinarily high incidence in the hemodialysis-dependent end-stage kidney disease population. There is a clear need to improve identification of those individuals at the highest risk of cardiov...

Deep Learning Algorithm of 12-Lead Electrocardiogram for Parkinson Disease Screening.

Journal of Parkinson's disease
BACKGROUND: Although idiopathic Parkinson's disease (IPD) is increasing with the aging population, there is no adequate screening test for early diagnosis of IPD. Cardiac autonomic dysfunction begins in the early stages of IPD, and an electrocardiogr...

A novel method for conformity assessment testing of electrocardiographs for post-market surveillance purposes.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Monitoring cardiac parameters is the fundamental aspect of every diagnostic process and is facilitated by electrocardiography (ECG) devices. This way, continuous state-of-the-art performance of ECG devices can be ensured. The new Medical ...

Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

Mayo Clinic proceedings
OBJECTIVE: To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the sub...

Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks.

Mathematical biosciences and engineering : MBE
Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addit...

[ST segment morphological classification based on support vector machine multi feature fusion].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and inter...

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.

Journal of the American College of Cardiology
BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgita...

A two-step method for paroxysmal atrial fibrillation event detection based on machine learning.

Mathematical biosciences and engineering : MBE
Detection of atrial fibrillation (AF) events is significant for early clinical diagnosis and appropriate intervention. However, in existing detection algorithms for paroxysmal AF (AFp), the location of AF starting and ending points in AFp is not conc...

A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical diagnosis. Automatic AF detection remains a very challenging task due to the high inter-patient varia...

A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a ...