Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status.

08:00 EDT 20th March 2020 | BioPortfolio

Summary of "Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status."

Pilots' brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteration, and then the hidden units of the gamma distribution are downsampled. An effective upper and lower Gibbs sampler is formed to realize the automatic reasoning of the network structure. In order to extract the 3-D instantaneous time-frequency distribution spectrum of electroencephalogram (EEG) signals and avoid signal modal aliasing, this article also proposes a smoothed pseudo affine Wigner-Ville distribution method. Finally, experimental results show that our model achieves satisfactory results in terms of both recognition accuracy and stability.


Journal Details

This article was published in the following journal.

Name: IEEE transactions on cybernetics
ISSN: 2168-2275


DeepDyve research library

PubMed Articles [17113 Associated PubMed Articles listed on BioPortfolio]

Bayesian deep matrix factorization network for multiple images denoising.

This paper aims at proposing a robust and fast low rank matrix factorization model for multiple images denoising. To this end, a novel model, Bayesian deep matrix factorization network (BDMF), is pres...

Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network.

Protruding lesions of the small bowel vary in wireless capsule endoscopy (WCE) images, and their automatic detection may be difficult. We aimed to develop and test a deep learning-based system to auto...

Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.

To perform test-retest reproducibility analyses for deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influent...

Graph Sequence Recurrent Neural Network for Vision-based Freezing of Gait Detection.

Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease (PD), a neurodegenerative disorder which impacts millions of people around the world. Accurate assessment of FoG is cri...

Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network.

Atrial fibrillation (AF) is the most common heart arrhythmia, and 12-lead electrocardiogram (ECG) is regarded as the gold standard for AF diagnosis. Highly accurate diagnosis of AF based on 12-lead EC...

Clinical Trials [7905 Associated Clinical Trials listed on BioPortfolio]

Impact of Automatic Polyp Detection System on Adenoma Detection Rate

In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether t...

Deep-Learning for Automatic Polyp Detection During Colonoscopy

The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonos...

Automatic Segmentation of Polycystic Liver

Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applicat...

Bayesian Hemodynamics Model for Personalized Monitoring of Congestive Heart Failure Patients

Heart failure (HF) is a serious and challenging syndrome. Globally 26 million people are living with this chronic disease and the prevalence is still increasing. Besides this growing numbe...

Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI

Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accuratel...

Medical and Biotech [MESH] Definitions

A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)

Data processing largely performed by automatic means.

Detection of drugs that have been abused, overused, or misused, including legal and illegal drugs. Urine screening is the usual method of detection.

Abnormally rapid heartbeats originating from one or more automatic foci (nonsinus pacemakers) in the HEART ATRIUM but away from the SINOATRIAL NODE. Unlike the reentry mechanism, automatic tachycardia speeds up and slows down gradually. The episode is characterized by a HEART RATE between 135 to less than 200 beats per minute and lasting 30 seconds or longer.

Ascertaining of deception through detection of emotional disturbance as manifested by changes in physiologic processes usually using a polygraph.

Quick Search

DeepDyve research library

Searches Linking to this Article