Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease

Hornero, Roberto; Escudero, Javier; Fernández, Alberto; Poza, Jesús; Gómez, Carlos

doi:10.1109/tbme.2008.919872

Cited by 75 publications

(113 citation statements)

References 39 publications

(101 reference statements)

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“…These results are in agreement with previous research works that have applied non-linear methods to estimate the regularity of the AD patients' brain activity. 1,2,12,17 ApEn values were significantly lower in the EEG of AD patients at electrodes P3 and P4, 1 whereas statistically significant differences were found at P3, P4, O1 and O2 using SampEn. 2 Our results also showed that AD patients have lower LZC values than controls.…”

Section: Discussionmentioning

confidence: 75%

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Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

et al. 2009

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Section: Discussionmentioning

confidence: 75%

“…Moreover, it is noteworthy that a leave-one-out cross-validation procedure has been used in our study and in Hornero et al 17 , but not in the other ones. Despite the fact that the accuracy decreases with this procedure, it provides a nearly unbiased estimate of the true error rate of the classification method.…”

Section: Discussionmentioning

confidence: 90%

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

et al. 2009

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“…MEGs from those of healthy elderly subjects [5,7,8,14,26,27]. Moreover, since two of them are spectral features (MF and SpecEn) and the other two (LZC and SampEn) are non-linear analysis methods, the usefulness of the BSS and component selection procedure could be tested with both types of techniques.…”

Section: Feature Extractionmentioning

confidence: 99%

“…EEG and MEG have been analysed with several signal processing techniques to gain insight into AD [3][4][5]. For instance, spectral features have been used to quantify the abnormalities in the spectra of AD patients' EEGs and MEGs [3,[6][7][8]. Additionally, nonlinear analysis methods can provide useful information about the brain dynamics in this dementia [4,5,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, spectral features have been used to quantify the abnormalities in the spectra of AD patients' EEGs and MEGs [3,[6][7][8]. Additionally, nonlinear analysis methods can provide useful information about the brain dynamics in this dementia [4,5,[8][9][10]. Nevertheless, it is desirable to develop novel strategies to help in AD detection from the analysis of the electromagnetic brain activity [9,11,12].…”

Section: Introductionmentioning

confidence: 99%

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Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Escudero¹,

Hornero²,

Abásolo³

et al. 2009

Medical Engineering & Physics

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This work studied whether a blind source separation (BSS) and component selection procedure could increase the differences between Alzheimer's disease (AD) patients and control subjects' spectral and non-linear features of magnetoencephalogram (MEG) recordings. MEGs were acquired with a 148-channel whole-head magnetometer from 62 subjects (36 AD patients and 26 controls), who were divided randomly into training and test sets. MEGs were decomposed using the algorithm for multiple unknown signals extraction (AMUSE). The extracted AMUSE components were characterised with two spectral -median frequency and spectral entropy (SpecEn) -and two non-linear features: Lempel-Ziv complexity (LZC) and sample entropy (SampEn). One-way analyses of variance with age as a covariate were applied to the training set to decide which components had the most significant differences between groups. Then, partial reconstructions of the MEGs were computed with these significant components. In the test set, the accuracy and area under the ROC curve (AUC) associated with each partial reconstruction of the MEGs were compared with the case where no BSS-preprocessing was applied. This preprocessing increased the AUCs between 0.013 and 0.227, while the accuracy for SpecEn, LZC and SampEn rose between 6.4% and 22.6%, improving the separation between AD patients and control subjects.

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Adaptive control and signal processing literature survey (No. 5)

2008

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please

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Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease

Cited by 75 publications

References 39 publications

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Adaptive control and signal processing literature survey (No. 5)

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