AIMC Topic: Electrocardiography

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Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

Heart rhythm
BACKGROUND: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.

Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

BMC medical informatics and decision making
BACKGROUND: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease...

Flavonoid bioactive compounds of hawthorn extract can promote growth, regulate electrocardiogram waves, and improve cardiac parameters of pulmonary hypertensive chickens.

Poultry science
The effect of orally administered hawthorn flavonoid extract (HFE) on growth, electrocardiographic waves, and cardiac parameters of pulmonary hypertensive chickens reared at high altitude (2,100 m above sea level) was examined. A total of 225 one-day...

A 13.34 μW Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors.

IEEE transactions on biomedical circuits and systems
Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However, the implementation of ultralow power ANN-CAC is challenging due to the intensive computations. Mor...

[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

Nederlands tijdschrift voor geneeskunde
In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral anticoagulation, instead of anti-platelet therapy, to decrease the chance of a recurrent ischaemic cer...

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.

Journal of electrocardiology
BACKGROUND: The electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasive nature. Arrhythmia can be classified into many types, including life-threatening and non-life-thr...

An incremental learning system for atrial fibrillation detection based on transfer learning and active learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a type of arrhythmia with high incidence. Automatic AF detection methods have been studied in previous works. However, a model cannot be used all the time without any improvement. And updating mod...

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE.

Australasian physical & engineering sciences in medicine
Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of ele...

Coronary artery calcium score quantification using a deep-learning algorithm.

Clinical radiology
AIM: To investigate the impact of a deep-learning algorithm on the quantification of coronary artery calcium score (CACS) and the stratification of cardiac risk.