A previously introduced variation of a conventional P300 speller, consisting on a modifiable image background and asymmetrically arranged stimulation markers for controlling wheelchair navigation, was used in this study. Five commonly used classifiers for solving P300 speller-like tasks, namely, Linear-SVM, RBF-SVM, LASSO-LDA, Shrinkage-LDA and SWLDA, were designed and trained and their performances contrasted, seeking the classifier with highest performance on our proposed screen. 19 able-bodied subjects participated in this study. The highest median sensitivity and specificity were respectively 1.00 (IQR = 0.61-1.00) and 1.00 (IQR = 0.96-1.00), which were obtained with the LASSO approach. These performances are suitable for the planned application and they are comparable with the conventional P300 speller performances reported, despite of our speller variation. Friedman tests showed that there are no statistical differences on the sensitivity and specificity performances among the five classifiers evaluated. However, the customized selection of the classifier approach improves the sensitivity by 66.7% in some cases.
KEYWORDS: Electroencephalography, Heart, Brain, Chest, Statistical analysis, Linear filtering, Modulation, Electronic filtering, Sensors, Process control
To search for possible interactions between the autonomic heart control and the brain activity we have evaluated the relation between heart rate variability (HRV) and the alpha and beta EEG band power dynamics. The experiments consisted in the alteration of HRV induced by respiratory rhythm changes. Modifications in the time series of the spectral density in each band {causally related to the HRV{ were recognized in the central and occipital regions using a Granger causality approach. The number of subjects and distribution of channels in which there was a causal relation changed with the different tasks assigned to the subjects and the analyzed bands. The causal relation between HRV and beta power series was observed in at least one channel, in eight out of nine subjects for a resting condition. On the other hand, in the HRV and alpha power series analysis the change in distribution was less pronounced, and detected in a smaller number of subjects. The results suggest that the HRV may be associated with the spectral band power time series dynamics, and this relationship is altered by the respiration.
We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.
KEYWORDS: Brain, Magnetic resonance imaging, Image segmentation, Neuroimaging, Alzheimer's disease, 3D modeling, 3D metrology, Feature extraction, Medical imaging, Pathology
Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.
In this paper, a nonparametric statistical segmentation procedure based on the computation of the mean shift within the joint space-range feature representation of brain MR images is presented. The mean shift is a simple, nonparametric estimator, which can be implemented in a data-driven approach. The number of classes and other initialization parameters are not needed to compute the mean shift. The procedure estimates the local modes of the probability density function in order to define the cluster centers on the feature space. Local segmentation quality is improved by including a measure of edge confidence among adjacent segmented regions. This measure drives the iterative application of transitive closure operations on the region adjacency graph until convergence to a stable set of regions. In this manner, edge detection and region segmentation techniques are combined for the extraction of weak but significant edges from brain images. With the proposed methodology, the modes of the classes' distribution can be robustly estimated and homogeneous regions defined, but also fine borders are preserved. The main contribution of this work is the combined use of mean shift estimation, together with a robust, edge-oriented region fusion technique to delineate structures in brain MRI.
A segmentation procedure using a radial basis function network (RBFN), coupled with an active contour (AC) model based on a cubic splines formulation is presented for the detection of the gray-white matter boundary in axial MMRI (T1, T2 and PD). A RBFN classifier has been previously introduced for MMRI segmentation, with good generalization at a rate of 10% misclassification over white and gray matter pixels on the validation set. The coupled RBFN and AC model system incorporates the posterior probability estimation map into the AC energy term as a restriction force. The RBFN output is also employed to provide an initial contour for the AC. Furthermore, an adaptation strategy for the network weights, guided by a feedback from the contour model adjustment at each iteration, is described. In order to compare the algorithm's performance, the segmentations using the adaptive, as well as the non-adaptive schemes were computed. It was observed that the major differences are located around deep circonvolutions, where the result of the adaptive process is superior than that obtained with the non-adaptive scheme, even in moderate noise conditions. In summary, the RBFN provides a good initial contour for the AC, the coupling of both processes keeps the final contour within the desired region and the adaptive strategy enhances the contour location.
Spatial quantification of relevant brain structures, is usually carried out through the analysis of a stack of magnetic resonance (MR) images by means of some image segmentation approach. In this paper, multispectral MR imaging segmentation based on a modified radial-basis function network is presented. Multispectral MR image sets are constructed by collecting data for the same anatomical structures under T1, T2 and FLAIR excitation sequences. Classification features for the network are extended beyond the normalized intensities in each band to also include the cylindrical coordinates of the image pixels. Such coordinates are determined within a reference image space upon which all targets are registered to. The network classifier was designed to differentiate three structures: gray matter, white matter and image background. The classification layer was also modified to accommodate the pixel cylindrical coordinates as inputs. With the designed network, background pixels are correctly classified for all cases, while gray and white matter pixels are misclassified for about 10% of the cases in the validation set. The source of these errors can be traced to smooth transitions in the output nodes for these two classes. Thresholding the outputs of these nodes to include a reject class reduces the misclassification error. The small and simple architecture of the network shows good generalization, and thus good segmentation over unseen stacks.
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