Search results for "Artificial"
showing 10 items of 7394 documents
Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
2018
Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ dat…
Functional Near Infrared Spectroscopy System Validation for Simultaneous EEG-FNIRS Measurements
2019
Functional near-infrared spectroscopy (fNIRS) applied to brain monitoring has been gaining increasing relevance in the last years due to its not invasive nature and the capability to work in combination with other well–known techniques such as the EEG. The possible use cases span from neural-rehabilitation to early diagnosis of some neural diseases. In this work a wireline FPGA–based fNIRS system, that use SiPM sensors and dual-wavelength LED sources, has been designed and validated to work with a commercial EEG machine without reciprocal interference.
Empirical Mode Decomposition on Mismatch Negativity
2008
Empirical mode decomposition (EMD) has been applied in the various disciplines to extract the desired signal. The basic principle is to decompose a time series into intrinsic mode functions (IFMs) and each IFM corresponds to an oscillation phenomenon. A statistical description of the oscillatory activities of the EEG has been well known. It is desired to extract single oscillatory process from the EEG by EMD. Mismatch negativity (MMN) can be automatically elicited by the deviant stimulus in an oddball paradigm, in which physically the deviant stimulus occurs among repetitive and homogeneous stimuli. MMN thus reflects the ability of the brain to detect changes in auditory stimuli. So, the MM…
Extract Mismatch Negativity and P3a through Two-Dimensional Nonnegative Decomposition on Time-Frequency Represented Event-Related Potentials
2010
This study compares the row-wise unfolding nonnegative tensor factorization (NTF) and the standard nonnegative matrix factorization (NMF) in extracting time-frequency represented event-related potentials—mismatch negativity (MMN) and P3a from EEG under the two-dimensional decomposition The criterion to judge performance of NMF and NTF is based on psychology knowledge of MMN and P3a MMN is elicited by an oddball paradigm and may be proportionally modulated by the attention So, participants are usually instructed to ignore the stimuli However the deviant stimulus inevitably attracts some attention of the participant towards the stimuli Thus, P3a often follows MMN As a result, if P3a was large…
Real and simulated endoscopy of neurosurgical approaches in an anatomical model
1997
Endoscopy simulation is a new visualisation technique which can be used to visualise patient anatomy as seen through endoscopes. In the following, we describe the application of simulated endoscopy to neurosurgical endoscopic approaches. We assess different aspects of virtual images and their influence on the final result, as judged by clinicians. One of these aspects is the projective geometry which is used to render the 3D images. Rendering using stereographic projection leads to more realistic endoscopic views than perspective projection.
Study of CT/MRI mutual information based registration applied in brachytherapy
2016
The present work aims to include magnetic resonance imaging (MRI) in a Medical Image-based Graphical platfOrm - Brachytherapy module (AMIGOBrachy) which coupled to the Monte Carlo N-Particle (MCNP6) code allows absorbed dose calculations. Computed tomography (CT) and MRI images were registered using mutual information algorithms to improve tissue segmentation potentially leading to a more accurate treatment planning system.
Towards data-driven medical imaging using natural language processing in patients with suspected urolithiasis.
2020
Abstract Objective The majority of radiological reports are still written as free text and lack structure. Further evaluation of free-text reports is difficult to achieve without a great deal of manual effort, and is not possible in everyday clinical practice. This study aims to automatically capture clinical information and positive hit rates from narrative radiological reports of suspected urolithiasis using natural language processing (NLP). Methods Narrative reports of low dose computed tomography (CT) of the retroperitoneum from April 2016 to July 2018 (n = 1714) were analyzed using NLP. These free-text reports were automatically structured based on RadLex concepts. Manual feedback was…
Oleic Acid-Injection in Pigs As a Model for Acute Respiratory Distress Syndrome
2018
The acute respiratory distress syndrome is a relevant intensive care disease with an incidence ranging between 2.2% and 19% of intensive care unit patients. Despite treatment advances over the last decades, ARDS patients still suffer mortality rates between 35 and 40%. There is still a need for further research to improve the outcome of patients suffering from ARDS. One problem is that no single animal model can mimic the complex pathomechanism of the acute respiratory distress syndrome, but several models exist to study different parts of it. Oleic acid injection (OAI)-induced lung injury is a well-established model for studying ventilation strategies, lung mechanics and ventilation/perfus…
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…