Search results for "Neural"
showing 10 items of 2783 documents
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
2022
Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolut…
Exploring Oscillatory Dysconnectivity Networks in Major Depression During Resting State Using Coupled Tensor Decomposition
2022
Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constr…
Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression
2021
To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscil…
Culture and odor categorization : agreement between cultures depends upon the odors
2003
This study evaluated the effect of culture on the relationship between psychological dimensions underlying odor perception and odor categorization. In a first experiment, French, Vietnamese and American participants rated several perceptual dimensions of everyday odorants, and sorted these odorants on the basis of their similarity. Results showed that the three groups of participants differed in their perceptual judgments but agreed in categorizing the odors into four consensual groups (floral, sweet, bad, and nature). Three dimensions––pleasantness, edibility, cosmetic acceptability––discriminated these groups in the same way in the three countries. In a second experiment, the participants…
Toward a Mature Science of Consciousness
2018
In \textit{Being No One}, Thomas \citet{Metzinger2003being} introduces an approach to the scientific study of consciousness that draws on theories and results from different disciplines, targeted at multiple levels of analysis. Descriptions and assumptions formulated at, for instance, the phenomenological, representationalist, and neurobiological levels of analysis provide different perspectives on the same phenomenon, which can ultimately yield necessary and sufficient conditions for applying the concept of phenomenal representation. In this way, the ``method of interdisciplinary constraint satisfaction (MICS)'' (as it has been called by Josh Weisberg, \citeyear{Weisberg2005consciousness})…
Artificial Neural Network Based Abdominal Organ Segmentations: A Review
2015
There are many neural network based abdominal organ segmentation approaches from medical images. Computed tomography images were mostly used in these approaches. Applied techniques are usually based on prior information regarding position, shape, and size of organs in these methods. In the literature, there are only a few neural network based techniques that were implemented to segment abdominal organs from magnetic resonance based images. In this paper, we present these methods and their results.
Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks
2019
Deep learning has contributed greatly to functional magnetic resonance imaging (fMRI) analysis, however, spatial maps derived from fMRI data by independent component analysis (ICA), as promising biomarkers, have rarely been directly used to perform individualized diagnosis. As such, this study proposes a novel framework combining ICA and convolutional neural network (CNN) for classifying schizophrenia patients (SZs) and healthy controls (HCs). ICA is first used to obtain components of interest which have been previously implicated in schizophrenia. Functionally informative slices of these components are then selected and labelled. CNN is finally employed to learn hierarchical diagnostic fea…
Monoclonal antibodies to polysialic acid reveal epitope sharing between invasive pathogenic bacteria, differentiating cells and tumor cells
1987
Monoclonal antibodies (mAb) for rapid diagnosis and detection of invasive bacteria and identification of pathogenic factors in infectious disease are equally important in medical microbiology and clinical pathology and may even provide a breakthrough in basic medical and cell biology research. Such a situation evolved from the application of a unique mAb against the poorly immunogenic homopolymers of alpha 2,8-linked sialic acid of Escherichia coli K1 and meningococci group B capsules which could be derived from immune-hyperreactive NZB-autoimmune mice. The cross-reactivity of this mAb with identical polysialic acid (polySA) units of the neural cell adhesion molecule (N-CAM) revealed antige…
Is neural network better than logistic regression in death prediction in patients after ST-segment elevation myocardial infarction?
2021
Background: There is a need to develop patient classification methods to adjust post-discharge care, improving survival after ST-segment elevation myocardial infarction (STEMI). Aims: The study aimed to determine whether a neural network (NN) is better than logistic regression (LR) in mortality prediction in STEMI patients. Material and methods: The study included patients from the Polish Registry of Acute Coronary Syndromes (PL-ACS). Patients with the first anterior STEMI treated with the primary percutaneous coronary intervention (pPCI) of the left anterior descending (LAD) artery between 2009 and 2015 and discharged alive were included in the study. Patients were randomly divided into th…
[Paediatric cochlear implantation in the critical period of the auditory pathway, our experience].
2009
Numerous experimental and clinical studies have suggested a critical or sensitive period in which the auditory pathway develops its greatest potential in terms of plasticity and learning. Early cochlear implantation performed in prelingual deaf children in this period provides a better prognosis for language acquisition. The aim of this study is to show the importance of cochlear implantation before this critical period ends.We conducted an observational, longitudinal, retrospective study of 57 children suffering profound prelingual bilateral sensorineural hearing loss who had received Advanced Bionics implants at our ENT department between June, 1998, and November, 2006. Data on their audi…