Arrhythmias

ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy.

BACKGROUND: Phospholamban (PLN) p.(Arg14del) variant carriers are at risk for development of malignant ventricular arrhythmia (MVA). Accurate risk stratification allows timely implantation of intracardiac defibrillators and is currently performed with a multimodality prediction model.

Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False-Positive Alerts.

BACKGROUND: Implantable loop recorders (ILRs) are increasingly placed for arrhythmia detection. However, historically, ≈75% of ILR alerts are false positives, requiring significant time and effort for adjudication. The LINQII and LUX-Dx are remotely reprogrammable ILRs with dual-stage algorithms using artificial intelligence to reduce false positives, but their utility in routine clinical practice...

Person identification with arrhythmic ECG signals using deep convolution neural network.

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracti...

Towards an EKG for SBO: A Neural Network for Detection and Characterization of Bowel Obstruction on CT.

A neural network was developed to detect and characterize bowel obstruction, a common cause of acute abdominal pain. In this retrospective study, 202 ...

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRg...

[Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?].

The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI ...

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments.

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detec...

A Q-transform-based deep learning model for the classification of atrial fibrillation types.

According to the World Health Organization (WHO), Atrial Fibrillation (AF) is emerging as a global epidemic, which has resulted in a need for techniqu...

Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians.

The rapid evolution of neural networks and deep learning has revolutionized various fields, with clinical cardiology being no exception. As traditiona...

Improving deep-learning electrocardiogram classification with an effective coloring method.

Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal rol...

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.

BACKGROUND: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However...

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using...

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification.

Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such...

Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications.

Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless ...

Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals.

Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives...

A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.

The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged a...

CLINet: A novel deep learning network for ECG signal classification.

Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the effi...

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for...

A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.

BACKGROUND: Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachyc...

A novel deep learning approach for early detection of cardiovascular diseases from ECG signals.

Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical diagnosis. Despite individuals' normal outward appea...