scholarly journals Principal Components Analysis of EEG Signals for Epileptic Patient Identification

Computation ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 133
Author(s):  
Maria Camila Guerrero ◽  
Juan Sebastián Parada ◽  
Helbert Eduardo Espitia
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fast Fourier Transformation ◽  
Patient Identification ◽  
Brain Disorder ◽  
Signal Features ◽  
Before And After ◽  
Neuronal Connections ◽  
Epilepsy Diagnosis

According to the behavior of its neuronal connections, it is possible to determine if the brain suffers from abnormalities such as epilepsy. This disease produces seizures and alters the patient’s behavior and lifestyle. Neurologists employ the electroencephalogram (EEG) to diagnose the disease through brain signals. Neurologists visually analyze these signals, recognizing patterns, to identify some indication of brain disorder that allows for the epilepsy diagnosis. This article proposes a study, based on the Fourier analysis, through fast Fourier transformation and principal component analysis, to quantitatively identify patterns to diagnose and differentiate between healthy patients and those with the disease. Subsequently, principal component analysis can be used to classify patients, employing frequency bands as the signal features. Besides, it is made a classification comparison before and after using principal component analysis. The classification is performed via logistic regression, with a reduction from 5 to 4 dimensions, as well as from 8 to 7, achieving an improvement when there are 7 dimensions in the precision, recall, and F1 score metrics. The best results obtained, without PCA are: precision 0.560, recall 0.690, and F1 score 0.620; meanwhile, the best values obtained using PCA are: precision 0.734, recall 0.787, and F1 score 0.776.

scholarly journals Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shengkun Xie ◽  
Sridhar Krishnan
Keyword(s):  
Principal Component Analysis ◽  
Single Channel ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Detection Methods ◽  
Moving Window ◽  
Feature Extraction Method ◽  
Epilepsy Diagnosis ◽  
Dynamic Principal Component Analysis

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

scholarly journals A Robust Procedure for Damage Detection from Strain Measurements Based on Principal Component Analysis

2013 ◽  
Vol 558 ◽  
pp. 128-138 ◽  
Author(s):  
Alfredo Guemes ◽  
J. Sierra-Pérez ◽  
J. Rodellar ◽  
L. Mujica
Keyword(s):  
Principal Component Analysis ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Complex Data ◽  
Data Set ◽  
Strain Measurements ◽  
Before And After ◽  
Small Cracks ◽  
Pca Algorithm

FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie.

Characteristics of Posed Smiles for Class III Female Patients Before and After Osteotomy Using Principal Component Analysis

2016 ◽  
Vol 27 (7) ◽  
pp. 1754-1758 ◽  
Author(s):  
Katsuaki Mishima ◽  
Mami Shiraishi ◽  
Yui Kawai ◽  
Hirotsugu Umeda ◽  
Hiroyuki Nakano ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Class Iii ◽  
Female Patients ◽  
Before And After

scholarly journals Analysis of Geomagnetic Ap Index on Worldwide Earthquake Occurrence using the Principal Component Analysis and Hierarchical Cluster Analysis

2021 ◽  
Vol 50 (4) ◽  
pp. 1157-1164
Author(s):  
Nur Hidayah Ismail ◽  
Nazhatulshima Ahmad ◽  
Nur Anisah Mohamed ◽  
Mohammad Redzuan Tahar
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Research Centre ◽  
Before And After ◽  
Solar Events ◽  
Ap Index

Geoeffective solar events, especially the coronal mass ejection (CME) and the high-speed solar wind (HSSW) will induce geomagnetic storm upon its arrival to Earth. The solar events could trigger an earthquake occurred during the arrival. In this study, the focus is on the proxy of the geoeffective solar events, which is the geomagnetic Ap index and the data of shallow worldwide earthquakes. The main objective was to investigate the impact of geomagnetic storms on the occurrences of earthquakes from 1994 to 2017 from a statistical perspective. The geomagnetic Ap index data was obtained from the Helmholtz-Centre Postdam - GFZ German Research Centre for Geosciences and the shallow worldwide earthquake data were from the United States Geological Survey (USGS) earthquake catalogue. The Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used to analyse the data. Two groups were obtained from the PCA biplot: Group 1 - before the event (Day-4 to Day-1) and Group 2 - after the event group (Day 0 to Day+4). A two-cluster solution was obtained from the HCA, which shows that days before and after geostorm are divided into two main clusters. The statistical results show that earthquakes activity might have different behaviour before and after the geostorm occurred. In conclusion, the results emphasize that there are differences between days before and after the geostorm occurrence, hence, the solar influence upon earthquake occurrences cannot be neglected entirely.

scholarly journals Application of Principal Component Analysis on NIR Spectral Collection after Elimination of Interference by a Least-Squares Procedure

1988 ◽  
Vol 42 (6) ◽  
pp. 1020-1023 ◽  
Author(s):  
M. F. Devaux ◽  
D. Bertrand ◽  
P. Robert ◽  
M. Qannari
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Water Content ◽  
Correlation Coefficient ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Spectral Interference ◽  
Before And After ◽  
Spectral Patterns

In NIR spectroscopy, multidimensional analyses such as Principal Component Analysis (PCA) may be applied to examine the similarity between spectra of natural products. However, such an approach is often limited by the effect of spectral interference due to water or particle size distribution of the samples. In the present work, the advantage of the elimination of such spectral interference before performing PCA was investigated. Unwanted component spectra were eliminated by a least-squares procedure. They were first orthogonalized and normalized by the Gram-Schmidt orthogonalization method. The subtraction coefficients were then assessed, similarly to principal component (PC) scores, by projection of the NIR spectra on the orthogonalized component spectra, and PCA was performed on the corrected spectra. This method was applied on an illustrative collection of wheat semolina conditioned in three levels of water content. Water was the component to be eliminated and had been previously modeled by two spectral patterns. These spectral patterns were used as the unwanted component spectra. PCA was applied independently before and after spectral correction of the collection of spectra and graphs obtained by the two procedures were compared. The squared correlation coefficient of the 3 first PC scores with water content was 0.979 before correction, with the 3 groups of water content appearing clearly on PCA graphs. After correction, the corresponding squared correlation coefficient for the 7 first PC scores was 0.016. PCA graphs obtained with corrected spectra also showed that the water effect was completely eliminated. At this moment, samples were separated according to their technological nature. The procedure developed may be useful in pattern recognition study and for automatic clustering of NIR spectra. It may also be applied in fields other than NIR spectroscopy.

Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis

2000 ◽  
Vol 92 (6) ◽  
pp. 1545-1552 ◽  
Author(s):  
Petra Bischoff ◽  
Eckehard Scharein ◽  
Gunter N. Schmidt ◽  
Georg von Knobelsdorff ◽  
Burkhart Bromm ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Frequency Band ◽  
Principle Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fast Fourier Transformation ◽  
Complex Data ◽  
Multivariate Statistical ◽  
Double Blind ◽  
Principle Component

Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. Results Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. Conclusions Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

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