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Arrhythmias, Cardiac

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Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

Computational and mathematical methods in medicine
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. T...

A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals.

Cardiovascular engineering and technology
PURPOSE: Wearable devices in the scenario of connected home healthcare integrated with artificial intelligence have been an effective alternative to the conventional medical devices. Despite various benefits of wearable electrocardiogram (ECG) device...

Expert-enhanced machine learning for cardiac arrhythmia classification.

PloS one
We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic c...

Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks.

Journal of the American Heart Association
Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we...

Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination.

Computer methods and programs in biomedicine
OBJECTIVE: We propose a new capsule network to compensate for the information loss in the deep convolutional networks in previous studies, and to improve the performance of arrhythmia classification.

Efficacy and Safety of Appropriate Shocks and Antitachycardia Pacing in Transvenous and Subcutaneous Implantable Defibrillators: Analysis of All Appropriate Therapy in the PRAETORIAN Trial.

Circulation
BACKGROUND: The PRAETORIAN trial (A Prospective, Randomized Comparison of Subcutaneous and Transvenous Implantable Cardioverter Defibrillator Therapy) showed noninferiority of subcutaneous implantable cardioverter defibrillator (S-ICD) compared with ...

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Medical & biological engineering & computing
Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple f...

Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.

Computational intelligence and neuroscience
The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning tech...

Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure.

ESC heart failure
AIMS: Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can handle a large volume of complex data more effectively than traditional statistical methods. This study...

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records.

PloS one
BACKGROUND: Cardiac dysrhythmias (CD) affect millions of Americans in the United States (US), and are associated with considerable morbidity and mortality. New strategies to combat this growing problem are urgently needed.