Alzheimer's diagnosis using eigenbrains and support vector machines

Álvarez, Ignacio; Górriz, Juan Manuel; Ramı́rez, Javier; Salas-González, D.; López, Manuel López; Puntonet, Carlos G.; Segovia, F.

doi:10.1049/el.2009.3415

Cited by 59 publications

(16 citation statements)

References 8 publications

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“…Revisiting the hippocampus part in the first eigenbrain of all key-slices, it is easily perceived that the body lateral ventricles area of AD are highlighted, which is indeed a distinct attribute between AD and NC. Our experiment extends the eigenbrain on SPECT images by Alvarez et al ( 2009a ) and Lopez et al ( 2009 ) and shows that eigenbrain is also suitable for MRI scans.…”

Section: Discussionsupporting

confidence: 80%

“…We proposed a visual interpretation method of Eigenbrain to detect regions that can distinguish AD and NC, which is not reported in literatures of Alvarez et al ( 2009a ) and Lopez et al ( 2009 ). The interpretation in a four-stage process is listed in Table 1 .…”

Section: The Proposed Methodsmentioning

confidence: 97%

“…The advantages of multivariate approaches are that they are data driven, which means that the analyses are fully based on the data without any prior knowledge and that the interactions among voxels and error effects are assessed statistically. However, multivariate approaches suffer from either the curse of dimension problem or the small sample size problems or the lack of the capability, to make statistical inferences about regionally specific changes (Álvarez et al, 2009b ).…”

Section: Introductionmentioning

confidence: 99%

“…The Eigenbrain was an excellent multivariate approach that solves both the curse of dimensionality and the problems in small sample size. It was proposed by Alvarez et al ( 2009a ) and Lopez et al ( 2009 ), and was applied on Single Photon Emission Computed Tomography (SPECT) images. In their research, the eigenbrain approach was shown to efficiently reduce the feature space from ~5 × 10 5 to only ~10 2 , and therefore, was able to achieve excellent classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

Zhang

Dong

Phillips

et al. 2015

Front. Comput. Neurosci.

222

120

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Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.Method: First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.Results: The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.Conclusion: The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.

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Section: Discussionsupporting

confidence: 80%

Section: The Proposed Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

Zhang

Dong

Phillips

et al. 2015

Front. Comput. Neurosci.

222

120

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“…PCA is also used in [3] to reduce the dimension of EEG data when identifying cases of epileptic seizure. It is also worth reporting that the term eigenbrain has already been employed in other applications performing PCA on brain related data such as in [15], where single photon emission computed tomography (SPECT) brain images are processed to diagnose Alzheimer deseases, and in [16], where an ap proximated model for representing visually-evoked potentials (VEPs) is proposed. Nonetheless, PCA and EBs are here employed for the first time for defining biometric identifiers based on a spectral representation of EEG signals.…”

Section: Eeg Templates For User Recognition: Eigenbrain Projectionsmentioning

confidence: 99%

EEG-based biometric recognition using EigenBrains

Maiorana

Rocca

Campisi

2015

2015 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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An increased level of attention has recently raised on biometric recognition by means of electroencephalography (EEG). This modality in fact possesses several properties which may be appealing for automatic people recognition, such as the intrinsic liveness detection and the robustness against potential attacks. Moreover, it could be easily exploited in applications based on brain-computer interfaces (BCI). In this paper we exhaustively analyze the discriminative capability of a compact representation of EEG signals acquired in resting conditions. Specifically, the exploited templates are obtained as projections into a subspace defined through EigenBrains (EBs), a basis for EEG data relying on principal component analysis (PCA). An extensive set of experimental tests, conducted on a database comprising 60 users, is performed to evaluate the recognition capabilities of the proposed representation under different system configurations

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