Does EEG Montage Influence Alzheimer′s Disease  Electroclinic Diagnosis?

Trambaiolli, Lucas R.; Lorena, Ana Carolina; Fraga, Francisco J.; Kanda, Paulo Afonso Medeiros; Nitrini, Ricardo; Anghinah, Renato

doi:10.4061/2011/761891

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…Lastly, it was also observed that high accuracy and sensitivity levels could be obtained with the bipolar signals alone. Since the bipolar montage measures EEG regional potentials [10], an interhemispheric disconnect may indeed be present with AD.…”

Section: B Classifier Performancementioning

confidence: 99%

“…An EEG bipolar signal was obtained by subtracting the two bi-auricular referenced signals involved [10]. The following bipolar channels were used in this study: F3-F4, F7-F8, C3-C4, T3-T4, P3-P4, T5-T6, and O1-O2.…”

Section: A Participantsmentioning

confidence: 99%

“…With spectral analysis, the consensus is that EEG spectral power is reduced with AD in the alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) frequency bands, and increased in the delta (0.1-4 Hz) and theta (4)(5)(6)(7)(8) bands. Spectral coherence (a measure of brain connectivity), in turn, has shown reduced values in AD when studying interhemispheric interactions, and has also been shown to be related to disease progression [6].…”

mentioning

confidence: 99%

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EEG spectro-temporal modulation energy: A new feature for automated diagnosis of Alzheimer's disease

Trambaiolli

Falk

Fraga

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-There is recent indication that Alzheimer's disease (AD) can be characterized by atypical modulation of electrophysiological brain activity caused by fibrillar amyloid deposition in specific regions of the brain, such as those related to cognition and memory. In this paper, we propose to objectively characterize EEG sub-band modulation in an attempt to develop an automated noninvasive AD diagnostics tool. First, multi-channel full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma. The temporal amplitude envelope of each sub-band is then computed via a Hilbert transformation. The proposed 'spectro-temporal modulation energy' feature measures the rate with which each sub-band is modulated. Modulation energy features are computed for 19 referential EEG signals and seven bipolar signals. Salient features are then selected and used to train four different classifiers, namely, support vector machines, logistic regression, classification and regression trees, and neural networks. Experiments with a database of 34 participants, 22 of which have been clinically diagnosed with probable-AD, show a neural network classifier achieving over 91% accuracy, thus significantly outperforming a classifier trained with conventional spectral-based features.

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Section: B Classifier Performancementioning

confidence: 99%

Section: A Participantsmentioning

confidence: 99%

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confidence: 99%

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EEG spectro-temporal modulation energy: A new feature for automated diagnosis of Alzheimer's disease

Trambaiolli

Falk

Fraga

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Motivated by previous findings [23,24], we explore the use of support vector machines (SVM) for feature selection and classification. A complete description of these steps is beyond the scope of this paper and the interested reader is referred to [25] and [26] for more details on SVMs.…”

Section: Feature Selection and Classifier Designmentioning

confidence: 99%

“…Previous findings have suggested that i) EEG spectral power is reduced with AD in the alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) frequency bands, and increased in the delta (0.1-4 Hz) and theta (4-8 Hz) bands [7], ii) spectral coherence is decreased between the two hemispheres in the alpha and beta frequency bands [8], and iii) EEG pattern complexity [9][10][11] is reduced. More recently, a new promising biomarker was developed, which was termed "percentage modulation energy" (PME) [12].…”

Section: Introductionmentioning

confidence: 99%

Towards an EEG-based biomarker for Alzheimer's disease: Improving amplitude modulation analysis features

Fraga

Falk

Trambaiolli

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, an EEG-based biomarker for automated Alzheimer's disease (AD) diagnosis is described, based on extending a recently-proposed "percentage modulation energy" (PME) metric. More specifically, to improve the signal-to-noise ratio of the EEG signal, PME features were averaged over different durations prior to classification. Additionally, two variants of the PME features were developed: the "percentage raw energy" (PRE) and the "percentage envelope energy" (PEE). Experimental results on a dataset of 88 participants (35 controls, 31 with mild-AD and 22 with moderate AD) show that over 98% accuracy can be achieved with a support vector classifier when discriminating between healthy and mild AD patients, thus significantly outperforming the original PME biomarker. Moreover, the proposed system can achieve over 94% accuracy when discriminating between mild and moderate AD, thus opening doors for very early diagnosis.

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