Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α1 (7.5–9.5 Hz), α2 (9.5–12.5 Hz), and β (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

[1]  S. Herukka,et al.  [Early diagnosis of Alzheimer's disease]. , 2013, Duodecim; laaketieteellinen aikakauskirja.

[2]  F Chollet,et al.  [Early diagnosis of Alzheimer's disease]. , 2012, Revue neurologique.

[3]  Andrzej Cichocki,et al.  EEG paroxysmal gamma waves during Bhramari Pranayama: A yoga breathing technique , 2009, Consciousness and Cognition.

[4]  Andrzej Cichocki,et al.  Quantifying Statistical Interdependence by Message Passing on Graphs—Part II: Multidimensional Point Processes , 2009, Neural Computation.

[5]  Andrzej Cichocki,et al.  Quantifying Statistical Interdependence by Message Passing on Graphs—Part I: One-Dimensional Point Processes , 2009, Neural Computation.

[6]  Andrzej Cichocki,et al.  On the synchrony of steady state visual evoked potentials and oscillatory burst events , 2009, Cognitive Neurodynamics.

[7]  Andrzej Cichocki,et al.  Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals , 2009, BMC Neuroscience.

[8]  Andrzej Cichocki,et al.  EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts , 2008, Physiological measurement.

[9]  Leslie M. Kay,et al.  Olfactory system gamma oscillations: the physiological dissection of a cognitive neural system , 2008, Cognitive Neurodynamics.

[10]  Andrzej Cichocki,et al.  Split-test Bonferroni correction for QEEG statistical maps , 2008, Biological Cybernetics.

[11]  Andrzej Cichocki,et al.  Statistical Modeling and Analysis of Laser-Evoked Potentials of Electrocorticogram Recordings from Awake Humans , 2007, Comput. Intell. Neurosci..

[12]  Jürgen Kurths,et al.  Order patterns recurrence plots in the analysis of ERP data , 2007, Cognitive Neurodynamics.

[13]  Michel Le Van Quyen,et al.  Analysis of dynamic brain oscillations: methodological advances , 2007, Trends in Neurosciences.

[14]  A. Cichocki,et al.  Techniques for early detection of Alzheimer's disease using spontaneous EEG recordings , 2007, Physiological measurement.

[15]  Rémi Gervais,et al.  A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics , 2007, Neural Networks.

[16]  J. G. van Dijk,et al.  Memory activation enhances EEG abnormality in mild cognitive impairment , 2007, Neurobiology of Aging.

[17]  M. Goedert,et al.  A Century of Alzheimer's Disease , 2006, Science.

[18]  L. Mucke,et al.  100 Years and Counting: Prospects for Defeating Alzheimer's Disease , 2006, Science.

[19]  W. Singer,et al.  Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology , 2006, Neuron.

[20]  Y. Kwak,et al.  Quantitative EEG Findings in Different Stages of Alzheimer’s Disease , 2006, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[21]  Andrzej Cichocki,et al.  Early Detection of Alzheimer's Disease by Blind Source Separation, Time Frequency Representation, and Bump Modeling of EEG Signals , 2005, ICANN.

[22]  A. Cichocki,et al.  EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease , 2005, Clinical Neurophysiology.

[23]  Maren Grigutsch,et al.  EEG oscillations and wavelet analysis , 2005 .

[24]  Todd C. Handy,et al.  Event-related potentials : a methods handbook , 2005 .

[25]  R. Ihl,et al.  EEG and the Test for the Early Detection of Dementia with Discrimination from Depression (TE4D): a validation study , 2004, International journal of geriatric psychiatry.

[26]  Jaeseung Jeong.  EEG dynamics in patients with Alzheimer's disease , 2004, Clinical Neurophysiology.

[27]  N. Crone,et al.  Attention to a painful cutaneous laser stimulus modulates electrocorticographic event-related desynchronization in humans , 2004, Clinical Neurophysiology.

[28]  D A Bennett,et al.  State-specific projections through 2025 of Alzheimer disease prevalence , 2004, Neurology.

[29]  Claudio Del Percio,et al.  Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study , 2004, NeuroImage.

[30]  M. Rowan,et al.  Memory-related EEG power and coherence reductions in mild Alzheimer's disease. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[31]  B. Winblad,et al.  The Magnitude of Dementia Occurrence in the World , 2003, Alzheimer disease and associated disorders.

[32]  Alberto Fernández,et al.  Correlations of hippocampal atrophy and focal low-frequency magnetic activity in Alzheimer disease: volumetric MR imaging-magnetoencephalographic study. , 2003, AJNR. American journal of neuroradiology.

[33]  M. Browne,et al.  Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising , 2002, Clinical Neurophysiology.

[34]  W. Shankle,et al.  A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage Alzheimer's disease , 2002, Clinical Neurophysiology.

[35]  J. Kowalski,et al.  The Diagnostic Value of EEG in Alzheimer Disease: Correlation With the Severity of Mental Impairment , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[36]  V. Knott,et al.  Quantitative electroencephalography in Alzheimer's disease: comparison with a control group, population norms and mental status. , 2001, Journal of psychiatry & neuroscience : JPN.

[37]  S. Carozzo,et al.  Timing of disease progression by quantitative EEG in Alzheimer' s patients. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[38]  Lo J. Bour,et al.  Slowing on Quantitative Spectral EEG Is a Marker for Rate of Subsequent Cognitive and Functional Decline in Early Alzheimer Disease , 1998, Alzheimer disease and associated disorders.

[39]  Guido Rodriguez,et al.  Quantitative Electroencephalography and Regional Cerebral Blood Flow: Discriminant Analysis between Alzheimer’s Patients and Healthy Controls , 1998, Dementia and Geriatric Cognitive Disorders.

[40]  H Sattel,et al.  Discrimination of Alzheimer's disease and normal aging by EEG data. , 1997, Electroencephalography and clinical neurophysiology.

[41]  H. Soininen,et al.  Slow-wave activity in the spectral analysis of the electroencephalogram and volumes of hippocampus in subgroups of Alzheimer's disease patients. , 1996, Behavioral neuroscience.

[42]  O Almkvist,et al.  Quantitative electroencephalography power and coherence in Alzheimer's disease and mild cognitive impairment. , 1996, Dementia.

[43]  J. Pernier,et al.  Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human , 1996, The Journal of Neuroscience.

[44]  Thomas Dierks,et al.  Topography of the maximum of the amplitude of EEG frequency bands in dementia of the Alzheimer type , 1996, Biological Psychiatry.

[45]  D O Walter,et al.  Regional differences in brain electrical activity in dementia: use of spectral power and spectral ratio measures. , 1993, Electroencephalography and clinical neurophysiology.

[46]  Thomas Dierks,et al.  Dementia of the alzheimer type: Effects on the spontaneous EEG described by dipole sources , 1993, Psychiatry Research: Neuroimaging.