Search results for "Component analysis"
showing 10 items of 562 documents
Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis
2014
Background: Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA.New method: For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated …
Effect of changing pulse rate on profile parameters of perceptual thresholds and loudness comfort levels and relation to ECAP thresholds in recipient…
2010
Abstract: The Nucleus CI24RE Freedom device offers higher stimulation rates and lower noise levels in action potential measurements (ECAPs) than previous devices. A study including ten European implant teams showed that the effect of changes in rate from 250 to 3500 pulses per second on tilt and curvature of the T and C profiles is insignificant. When changing rate one may change the levels at all electrodes by the same amount. Using an automated procedure ECAPs could be measured quickly and reliably at a noise level of only 1 μV. However, this did not result in improved correlations between the tilt and curvature parameters of the ECAP profiles and those of the T and C profiles. Average C …
Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes
2015
[EN] Patients suffering from atrial fibrillation can be classified into different subtypes, according to the temporal pattern of the arrhythmia and its recurrence. Nowadays, clinicians cannot differentiate a priori between the different subtypes, and patient classification is done afterwards, when its clinical course is available. In this paper we present a comparison of classification performances when differentiating paroxysmal and persistent atrial fibrillation episodes by means of support vector machines. We analyze short surface electrocardiogram recordings by extracting modulus and phase features from several time-frequency transforms: short-time Fourier transform, Wigner-Ville, Choi-…
Revealing deterministic structures in click-evoked otoacoustic emissions.
2000
Click-evoked otoacoustic emissions (CEOAEs) were studied by means of recurrence quantification analysis (RQA) and were found to be endowed with a relevant amount of deterministic structuring. Such a structure showed highly significant correlation with the clinical evaluation of the signal over a data set including 56 signals. Moreover, 1) one of the RQA variables, Trend, was very sensitive to phase transitions in the dynamical regime of CEOAEs, and 2) appropriate use of principal component analysis proved able to isolate the individual character of the studied signals. These results are of general interest for the study of auditory signal transduction and generation mechanisms.
Centenarians, but not octogenarians, up-regulate the expression of microRNAs
2012
Centenarians exhibit extreme longevity and a remarkable compression of morbidity. They have a unique capacity to maintain homeostatic mechanisms. Since small non-coding RNAs (including microRNAs) are implicated in the regulation of gene expression, we hypothesised that longevity of centenarians may reflect alterations in small non-coding RNA expression. We report the first comparison of microRNAs expression profiles in mononuclear cells from centenarians, octogenarians and young individuals resident near Valencia, Spain. Principal Component Analysis of the expression of 15,644 mature microRNAs and, 2,334 snoRNAs and scaRNAs in centenarians revealed a significant overlap with profiles in you…
Air quality assessment via functional principal component analysis
2009
The knowledge of the global urban air quality situation represents the first step to face air pollution issues. For the last decades many urban areas can rely on a monitoring network, recording hourly data for the main pollutants. Such data need to be aggregated according to different dimensions, such as time, space and type of pollutant, in order to provide a synthetic air quality index which takes into account interactions among pollutants and correlation among monitoring sites.This paper focuses on Functional Principal Component techniques for the statistical analysis of a set of environmental data x(spt), where s stands for the monitoring site, p for the pollutant and t for time, usuall…
Principal component analysis for the selection of variables in the application of the H-point and generalised H-point standard addition method
2000
The present paper deals with the selection of variables for the H-point and generalised H-point standard additions methods (HPSAM and GHPSAM, respectively). Both methods are applied for the resolution of spectroscopic interfered signals in the UV-vis range. The HPSAM is a suitable method for the resolution of binary and ternary mixtures when the interferent is known. The GHPSAM is applied for the resolution of samples that contain unknown interferents. In this paper, a method based on the study of a principal components analysis (PCA) for the selection of variables for the HPSAM and GHPSAM is proposed. The PCA results show the isolation of the analyte signal from the sample signal, achieved…
Machine Learning-Based Classification of Vector Vortex Beams.
2020
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. O…
On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
2021
The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of n=86 patients underwent computed tomography (…
Statistical shape analysis of ascending thoracic aortic aneurysm: Correlation between shape and biomechanical descriptors
2020
An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allow…