Arrhythmias

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. In this study, we propose a multi-lead ensemble neural network (MENN) to distinguish anterior myocardial infarction (AMI) and inferior myocardial infarction (IMI) from healthy control (HC) respectively. In the study, three kinds of sub-networks and m...

Phase-domain Deep Patient-ECG Image Learning for Zero-effort Smart Health Security.

Smart health is quickly boosted by technological advancements: smart sensors, body sensor network, internet of medical things and big data. Vast amounts of smart health big data from ubiquitous sensors pose unprecedented challenges to the security and privacy protection, which is extremely critical in healthcare applications. The vital signs, user daily behaviors, medicine recommendations, and so ...

ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning.

Biometric technologies offer much convenience over the conventional approaches to identity recognition, but security and privacy concerns also accompa...

The Feasibility of Arrhythmias Detection from A Capacitive ECG Measurement Using Convolutional Neural Network.

Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between electrodes and skin. This unconstrained measurement is...

Cardiovascular disease diagnosis using cross-domain transfer learning.

While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via inspecting electrocardiogram (ECG) waveforms, these decisions can be ...

Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum.

Atrial Fibrillation (AF) is one of the arrhythmias that is common and serious in clinic. In this study, a novel method of AF classification with a con...

RespNet: A deep learning model for extraction of respiration from photoplethysmogram.

Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chro...

Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification.

Automatic classification of abnormal beats in ECG signals is crucial for monitoring cardiac conditions and the performance of the classification will ...

[Automatic classification method of arrhythmia based on discriminative deep belief networks].

Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is s...

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram.

IMPORTANCE: For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while g...

[Deep residual convolutional neural network for recognition of electrocardiogram signal arrhythmias].

Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations...

A new deep learning model for assisted diagnosis on electrocardiogram.

In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on el...

Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA.

Background We developed a new left ventricular hypertrophy ( LVH ) criterion using a machine-learning technique called Bayesian Additive Regression Tr...

Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm ...

Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding ...

[A DenseNet-based diagnosis algorithm for automated diagnosis using clinical ECG data].

OBJECTIVE: To train convolutional networks using multi-lead ECG data and classify new data accurately to provide reliable information for clinical dia...

An Ontology Approach for Knowledge Representation of ECG Data.

The number of features that can be extracted from ECG signals has increased with the advancement in signal processing techniques. At the same time, th...

Estimating Systolic Blood Pressure Using Convolutional Neural Networks.

Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve ...

Zerobot®: A Remote-controlled Robot for Needle Insertion in CT-guided Interventional Radiology Developed at Okayama University.

Since 2012, we have been developing a remote-controlled robotic system (Zerobot®) for needle insertion during computed tomography (CT)-guided interven...

Finger ECG based Two-phase Authentication Using 1D Convolutional Neural Networks.

This paper presents a study using 1D convolutional neural networks (CNNs) for ECG-based authentication. A simple CNN structure is used to both learn t...