Arrhythmias

A Feasible Feature Extraction Method for Atrial Fibrillation Detection From BCG.

Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here, we present a feature extraction method for the detection of AF using a ballistocardiogram (BCG), which is based on a physiological signal database co...

Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, primarily using electrocorticography (ECoG). To develop seizure forecasting for broad clinical and ambulatory use, however, less complex and invasive mod...

A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines.

Real-time wearable electrocardiogram monitoring sensor is one of the best candidates in assisting cardiovascular disease diagnosis. In this paper, we ...

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective sy...

Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings.

OBJECTIVE: We aim to combine deep neural networks and engineered features (hand-crafted features based on medical domain knowledge) for cardiac arrhyt...

In situ tissue classification during laser ablation using acoustic signals.

We suggest a novel method to classify the type of tissue that is being ablated, using the recorded acoustic sound waves during pulsed ultraviolet lase...

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification.

Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classifica...

Neural network model of an amphibian ventilatory central pattern generator.

The neuronal multiunit model presented here is a formal model of the central pattern generator (CPG) of the amphibian ventilatory neural network, insp...

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG featu...

Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to fin...

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.

INTRODUCTION: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for prima...

A new approach for arrhythmia classification using deep coded features and LSTM networks.

BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term ...

A RR interval based automated apnea detection approach using residual network.

BACKGROUND AND OBJECTIVE: Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, ...

The Big Picture on the "AI Turn" for Digital Health: The Internet of Things and Cyber-Physical Systems.

This article offers an analysis of the ways in which digital health innovations are being coproduced by mainstreaming of artificial intelligence (AI),...

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm.

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to ...

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart's ac...

Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture.

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the co...

Long-term results of monopolar versus bipolar radiofrequency ablation procedure for atrial fibrillation.

BACKGROUND: In this study, we aimed to evaluate the long-term outcomes of monopolar or bipolar radiofrequency ablation concomitant to mitral valve sur...

A novel ECG signal compression method using spindle convolutional auto-encoder.

BACKGROUND AND OBJECTIVES: With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate he...

ECG Multilead Interval Estimation Using Support Vector Machines.

This work reports a multilead interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG proc...