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At least 181 records · Page 10Linked to original sources
PubMed1998

Spontaneous variability reveals principal components in cortical evoked potentials.

Using principal component analysis, we studied trial to trial, spontaneous variability of evoked potentials (EPs) recorded from rat barrel cortex after whisker stimulation. This method allowed for extraction of two distinct components of EP which overlapped in the time domain. Our results are consonant with the previously described depth distribution of current sources and the extracted components can be therefore attributed to activities of two pyramidal cell classes: supra- and infragranular. Qualitatively similar results were found in both anaesthetized and alert animals.

Animals↗
PubMed1997

Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition.

Principal component analysis (PCA) by singular value decomposition (SVD) may be used to analyze an epoch of a multichannel electroencephalogram (EEG) into multiple linearly independent (temporally and spatially noncorrelated) components, or features; the original epoch of the EEG may be reconstructed as a linear combination of the components. The result of SVD includes the components, expressible as time series waveforms, and the factors that determine how much each component waveform contributes to each EEG channel. By omission of some component waveforms from the linear combination, a new EEG can be reconstructed, differing from the original in useful ways. For example, artifacts can be removed and features such as ictal or interictal discharges can be enhanced by suppressing the remainder of the EEG. We developed a variation of this technique in which the factors that reconstruct the modified EEG from the original are stored as a matrix. This matrix is applied to multichannel EEG at successive times to create a new EEG continuously in real time, without redoing the time-consuming SVD. This matrix acts as a spatial filter with useful properties. We successfully applied this method to remove artifacts, including ocular movement and electrocardiographic artifacts. Removal of myogenic artifacts was much less complete, but there was significant improvement in the ability to visualize underlying activity in the presence of myogenic artifacts. The major limitations of the method are its inability to completely separate some artifacts from cerebral activity, especially when both have similar amplitudes, and the possibility that a spatial filter may distort the distribution of activities that overlap with the artifacts being removed.

Data Interpretation, Statistical↗
PubMed1994

Synthetic images by subspace transforms. I. Principal components images and related filters.

The principal component (PC) approach offers compressions of an image sequence into fewer images and noise suppressing filters. Multiple MR images of the same tomographic slice obtained with different acquisition parameters (i.e., with different TR, TE, and flip angles), time sequences of images in nuclear medicine, and cardiac ultrasound image sequences are examples of such input image sets. In this paper noise relationships of original and linearly transformed image sequences in general, and specifically of original, PC, and PC-filtered images are discussed. As the spinoff, it introduces locally weighted PC transforms and filters, nonlinear PC's, and a single-image based filter for suppression of noise. Examples illustrate increased perceptibility of anatomical/functional structures in PC images and PC-filtered images, including extraction of physiological functional information by PC loading curves. Generally, the more correlated the original images are, the more effective is the PC approach.

Diagnostic Imaging↗
PubMed1997

Using principal component analysis to describe wound status.

Principal component (PC) analysis was used to assess the status of pressure ulcers over time in 37 subjects. Content validity was established by literature review and expert opinion. The primary variables in the wound healing model are ulcer surface area, exudate amount, and surface appearance. A linear function based on PC analysis of these values was established at each time point with an appropriate weighting of 2:3:3. This function explained 55% to 65% of the variation in the data. Other variables were considered in the model as well. However, because of the correlation of these variables with the original three, the modeling was not improved. A decreasing function from week 0 to week 8 established a good linear fit to the data and reasonable discrimination between time points, given the limitations of the sample size. There is good discrimination among the time points, at least for the earlier times compared with the later times. The procedure, although promising, requires validation with a larger data set.

Discriminant Analysis↗
PubMed1987

Principal components analysis of therapeutic orientations of doctoral programs in clinical psychology.

A principal components analysis was conducted on a data set that consisted of ratings of therapeutic orientations reported by directors of clinical psychology training programs in 96 clinical psychology programs in the U.S. Two principal components emerged, which contrasted (1) behavioral vs. psychoanalytic approaches and (2) humanistic vs. conditioning approaches. A plotting of factor scores revealed relatively clear separation among programs primarily identified with either behavioral or psychoanalytic/humanistic approaches. The majority of training programs, however, clustered around the midpoint on both underlying factor dimensions, an indication of the adoption of multiple approaches in training.

Curriculum↗
PubMed2005

Functional principal component analysis of fMRI data.

We describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used accordingly to estimate an image in which smooth functions replace the voxels. The techniques of functional data analysis are used to carry out PCA directly on these functions. We show that functional PCA is more effective than is its ordinary counterpart in recovering the signal of interest, even if limited or no prior knowledge of the form of hemodynamic function or the structure of the experimental design is specified. We discuss the rationale and advantages of the proposed approach relative to other exploratory methods, such as clustering or independent component analysis, as well as the differences from methods based on expanded design matrices.

Adult↗
PubMed2006

Modified secured principal component regression for detection of unexpected chromatographic features in herbal fingerprints.

Secured principal component regression is modified for the qualitative analysis of chromatographic fingerprint data sets of herbal samples with residual concentrations. After chromatographic shift-correction and autoscaling are performed on the data, this modified secured principal component regression (msPCR) can detect unexpected chromatographic features in various herbal fingerprints. The successful application of msPCR to two real herbal medicines of Erigeron breviscapus from different geographical origins and Ginkgo biloba from various sources or vendors demonstrates that the proposed method can detect reasonably unexpected features differing from the regulars or not being modeled. From a chemical point of view, the causes have also been explained to corroborate the results. Moreover, it presents a viable approach for the qualitative evaluation of diverse herbal objects with a regular class of chromatographic fingerprints.

Journal Article↗
PubMedDate not supplied

Principal component analysis for content-based image retrieval.

Most picture archiving and communication systems provide image search capabilities that support queries based on patient demographics and study descriptions. In a preliminary study, principal component analysis was used to represent and retrieve images on the basis of content. Principal component analysis reduces the dimensionality of the search to a basis set of prototype images that best describes the images. Each image is described by its projection on the basis set; a match to a query image is determined by comparing its projection vector on the basis set with that of the images in the database. The training image database consisted of 100 axial brain images from a three-dimensional T1-weighted magnetic resonance imaging study. The algorithm was evaluated by using 96 axial images from eight patients. Image retrieval was considered accurate if the automated algorithm returned the match section to within 3 mm of an expert-selected section; the retrieval accuracy was 83% when the images were preprocessed for uniformity in intensity and geometry. Principal component analysis can be applied to content-based retrieval of medical images. The algorithm is designed to be part of an automated image selection module that filters relevant images from an imaging study.

Algorithms↗
PubMed2003

A method for dynamic spectrophotometric measurements in vivo using principal component analysis-based spectral deconvolution.

A method was developed for dynamic spectrophotometric measurements in vivo in the presence of non-specific spectral changes due to external disturbances. This method was used to measure changes in mitochondrial respiratory pigment redox states in photoreceptor cells of live, white-eyed mutants of the blowfly Calliphora vicina. The changes were brought about by exchanging the atmosphere around an immobilised animal from air to N2 and back again by a rapid gas exchange system. During an experiment reflectance spectra were measured by a linear CCD array spectrophotometer. This method involves the pre-processing steps of difference spectra calculation and digital filtering in one and two dimensions. These were followed by time-domain principal component analysis (PCA). PCA yielded seven significant time domain principal component vectors and seven corresponding spectral score vectors. In addition, through PCA we also obtained a time course of changes common to all wavelengths-the residual vector, corresponding to non-specific spectral changes due to preparation movement or mitochondrial swelling. In the final step the redox state time courses were obtained by fitting linear combinations of respiratory pigment difference spectra to each of the seven score vectors. The resulting matrix of factors was then multiplied by the matrix of seven principal component vectors to yield the time courses of respiratory pigment redox states. The method can be used, with minor modifications, in many cases of time-resolved optical measurements of multiple overlapping spectral components, especially in situations where non-specific external influences cannot be disregarded.

Animals↗
PubMed2004

Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysis.

BACKGROUND AND AIMS: Petals are important for Primula sieboldii because of the commercial value of its flowers, and their form is a target characteristic for breeding. An appropriate understanding of petal form in terms of genetic mechanisms and environmental effects is necessary for improvement of this species. The aim of this study was to establish a quantitative evaluation method of petal shape by elliptic Fourier descriptors and principal component analysis (EF-PCA), and thus to investigate genotypic and environmental effects on petal morphology. METHODS: EF-PCA describes an overall shape mathematically by transforming coordinate information concerning its contours into elliptic Fourier descriptors (EFDs) and summarizing the EFDs by principal component analysis. To examine varietal effects on principal component (PC) scores and petal area among commercial varieties, nested ANOVAs were performed (since the samples had a hierarchical structure with four sources, i.e. variety, plant, flower and petal). KEY RESULTS: Petal shape variation could be evaluated successfully and the symmetrical and asymmetrical elements of the overall shape variation could be detected. The proportions of the variance component due to varietal differences were more than 70 % in the first five PCs of the symmetrical elements and petal area. By contrast, the proportions due to varietal effects of all PCs of the asymmetrical elements were less than 20 %, and the proportions of the variation within a flower were more than 75 %. It was also demonstrated that the yearly variance of petal shape was small, and that of petal area was large. CONCLUSIONS: Within a flower the major source of the symmetrical elements is genotypic and the asymmetrical elements are strongly affected by the environment. With respect to petal area, the contribution of genotypes is also large; it is, however, affected by the macro-environment more notably than is petal shape.

Analysis of Variance↗
PubMed2006

A novel incremental principal component analysis and its application for face recognition.

Principal component analysis (PCA) has been proven to be an efficient method in pattern recognition and image analysis. Recently, PCA has been extensively employed for face-recognition algorithms, such as eigenface and fisherface. The encouraging results have been reported and discussed in the literature. Many PCA-based face-recognition systems have also been developed in the last decade. However, existing PCA-based face-recognition systems are hard to scale up because of the computational cost and memory-requirement burden. To overcome this limitation, an incremental approach is usually adopted. Incremental PCA (IPCA) methods have been studied for many years in the machine-learning community. The major limitation of existing IPCA methods is that there is no guarantee on the approximation error. In view of this limitation, this paper proposes a new IPCA method based on the idea of a singular value decomposition (SVD) updating algorithm, namely an SVD updating-based IPCA (SVDU-IPCA) algorithm. In the proposed SVDU-IPCA algorithm, we have mathematically proved that the approximation error is bounded. A complexity analysis on the proposed method is also presented. Another characteristic of the proposed SVDU-IPCA algorithm is that it can be easily extended to a kernel version. The proposed method has been evaluated using available public databases, namely FERET, AR, and Yale B, and applied to existing face-recognition algorithms. Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%. This implies that the proposed SVDU-IPCA method gives a close approximation to the batch-mode PCA method.

Algorithms↗
PubMed2006

The principal components model: a model for advancing spirituality and spiritual care within nursing and health care practice.

AIM: The aim of this study was to generate a deeper understanding of the factors and forces that may inhibit or advance the concepts of spirituality and spiritual care within both nursing and health care. BACKGROUND: This manuscript presents a model that emerged from a qualitative study using grounded theory. Implementation and use of this model may assist all health care practitioners and organizations to advance the concepts of spirituality and spiritual care within their own sphere of practice. The model has been termed the principal components model because participants identified six components as being crucial to the advancement of spiritual health care. DESIGN: Grounded theory was used meaning that there was concurrent data collection and analysis. Theoretical sampling was used to develop the emerging theory. These processes, along with data analysis, open, axial and theoretical coding led to the identification of a core category and the construction of the principal components model. METHODS: Fifty-three participants (24 men and 29 women) were recruited and all consented to be interviewed. The sample included nurses (n=24), chaplains (n=7), a social worker (n=1), an occupational therapist (n=1), physiotherapists (n=2), patients (n=14) and the public (n=4). The investigation was conducted in three phases to substantiate the emerging theory and the development of the model. RESULTS: The principal components model contained six components: individuality, inclusivity, integrated, inter/intra-disciplinary, innate and institution. CONCLUSION: A great deal has been written on the concepts of spirituality and spiritual care. However, rhetoric alone will not remove some of the intrinsic and extrinsic barriers that are inhibiting the advancement of the spiritual dimension in terms of theory and practice. RELEVANCE TO CLINICAL PRACTICE: An awareness of and adherence to the principal components model may assist nurses and health care professionals to engage with and overcome some of the structural, organizational, political and social variables that are impacting upon spiritual care.

Adolescent↗
PubMed1990

Use of principal components analysis to develop a composite score as a primary outcome variable in a clinical trial. The VA Cooperative Study Group on Cochlear Implantation.

This article describes the use of principal components analysis to derive a composite score from a battery of 24 audiologic tests. The composite score is being used as the primary outcome variable in a clinical trial comparing the efficacy of three cochlear implant devices for people with bilateral, profound hearing loss. The first principal component from the within-class pooled variance-covariance matrix over four time periods was chosen to establish the coefficients for the composite score. This component accounted for 61% of the total variance of the 24 audiologic tests. The first principal component had its largest coefficients associated with the most difficult audiologic tests. The mean composite score of all patients improved over time; some patients showed dramatic improvement. The changes in the composite score over time were also closely related to subjective impressions of implant performance by the patient, audiologist, and otolaryngologist.

Analysis of Variance↗
PubMed1965

AUTOMATIC CLASSIFICATION OF STAPHYLOCOCCI BY PRINCIPAL-COMPONENT ANALYSIS AND A GRADIENT METHOD.

Hill, L. R. (Università Statale, Milano, Italy), L. G. Silvestri, P. Ihm, G. Farchi, and P. Lanciani. Automatic classification of staphylococci by principal-component analysis and a gradient method. J. Bacteriol. 89:1393-1401. 1965.-Forty-nine strains from the species Staphylococcus aureus, S. saprophyticus, S. lactis, S. afermentans, and S. roseus were submitted to different taxometric analyses; clustering was performed by single linkage, by the unweighted pair group method, and by principal-component analysis followed by a gradient method. Results were substantially the same with all methods. All S. aureus clustered together, sharply separated from S. roseus and S. afermentans; S. lactis and S. saprophyticus fell between, with the latter nearer to S. aureus. The main purpose of this study was to introduce a new taxometric technique, based on principal-component analysis followed by a gradient method, and to compare it with some other methods in current use. Advantages of the new method are complete automation and therefore greater objectivity, execution of the clustering in a space of reduced dimensions in which different characters have different weights, easy recognition of taxonomically important characters, and opportunity for representing clusters in three-dimensional models; the principal disadvantage is the need for large computer facilities.

Classification↗
PubMedDate not supplied

A general treatment of solubility. 3. Principal component analysis (PCA) of the solubilities of diverse solutes in diverse solvents.

A phenomenological study of solubility has been conducted using a combination of quantitative structure-property relationship (QSPR) and principal component analysis (PCA). A solubility database of 4540 experimental data points was used that utilized available experimental data into a matrix of 154 solvents times 397 solutes. Methodology in which QSPR and PCA are combined was developed to predict the missing values and to fill the data matrix. PCA on the resulting filled matrix, where solutes are observations and solvents are variables, shows 92.55% of coverage with three principal components. The corresponding transposed matrix, in which solvents are observations and solutes are variables, showed 62.96% of coverage with four principal components.

Journal Article↗
PubMed1991

Principal components analysis of regional bone density in black and white women: relationship to body size and composition.

Black and white women in the United States differ with respect to bone mass and the risk of developing osteoporosis. It has been suggested that greater body size among U.S. blacks may contribute to greater bone density in this group. It is not known whether the fat or lean component contributes more to this relationship. Bone density was measured at seven sites in 161 normal black and white women using single and dual photon absorptiometry. The first principal component accounted for 73% of the variance in the sample and constitutes an index of skeletal mass. The second principal component added another 10% and contrasts the axial and appendicular sites. Both the regional bone densities and the first principal component showed significantly greater bone densities for blacks; adjustment for body size reduced bone mass differences by approximately 50%. Body composition analysis done on a subset of these women indicated that the fat component of body mass may be the more important factor in its effect on bone mass.

Absorptiometry, Photon↗
PubMed2000

Principal components in three-ball cascade juggling.

To uncover the underlying control structure of three-ball cascade juggling, we studied its spatiotemporal properties in detail. Juggling patterns, performed at fast and preferred speeds, were recorded in the frontal plane and subsequently analyzed using principal component analysis and serial correlation techniques. As was expected on theoretical grounds, the principal component analysis revealed that maximally four instead of the original six dimensions (3 balls x 2 planar coordinates) are sufficient for describing the juggling dynamics. Juggling speed was shown to affect the number of dimensions (four for the fast condition, two for the preferred condition) as well as the smoothness of the time evolution of the eigenvectors of the principal component analysis, particularly around the catches. Contrary to the throws and the zeniths, and regardless of juggling speed, consecutive catches of the same hand showed a markedly negative lag-one serial correlation, suggesting that the catches are timed so as to preserve the temporal integrity of the juggling act.

Biomechanical Phenomena↗
PubMedDate not supplied

Principal component analysis of shape variables in adult individuals.

Human body shape variables were obtained by adjusting 34 distances between trunk/limbs, and head/face, landmarks, for an overall appropriate body size measurement. The adjustment was based on regression analysis. Principal component analysis was applied to thus defined shape variables to obtain shape dimensions in 99 normal adult males and 103 females. The first principal component either for trunk/limbs shape variables, or for head/face variables (considered separately in the analyses) is similar in both sexes in that it represents relative proportions between trunk and limb lengths and widths, and between midfacial lengths and widths, respectively. However there are appreciable differences in the succeeding components. The problem of interpretation of body shape dimensions, especially those accounting for less than 20% of the sample variance, as well as difficulties in assessing the biological meaning of dependence structures determined by principal component analysis in humans, are discussed.

Adolescent↗