Arrhythmias

A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement.

The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Therefore, we propose a new signal-processing framework that determines the signal quality for short signal segments (2 and 4 seconds) using a multi-class...

A Supervised Approach to Robust Photoplethysmography Quality Assessment.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust...

Cardiac Rhythm Device Identification Using Neural Networks.

OBJECTIVES: This paper reports the development, validation, and public availability of a new neural network-based system which attempts to identify th...

Comparisons of efficacy, safety, and recurrence risk factors of paroxysmal and persistent atrial fibrillation catheter ablation using robotic magnetic navigation system.

BACKGROUND: No data exist on comparisons of efficacy, safety, and recurrence risk factors of paroxysmal and persistent atrial fibrillation (AF) ablati...

Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiol...

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previou...

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram.

Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevit...

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algo...

Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection.

BACKGROUND AND OBJECTIVE: Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long t...

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.

OBJECTIVE: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four...

Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types.

Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and co...

Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning.

We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a g...

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation.

BACKGROUND: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis,...

Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things.

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of...

Radiofrequency endometrial ablation for treating heavy menstrual bleeding in women with chronic renal failure.

OBJECTIVE: The study objective was to retrospectively evaluate the efficacy and safety of radiofrequency endometrial ablation in treating heavy menstr...

Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection.

OBJECTIVE: Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of...

A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.

OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs...

Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmi...

ECG Signal Classification Using Various Machine Learning Techniques.

Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. I...

ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation.

Electrocardiogram (ECG) is gaining increased attention as a biometric method in a wide range of applications, such as access control and security/priv...