Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer’s Disease and Mild Cognitive Impairment

The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.

[1]  Roberto Hornero,et al.  MEG analysis of neural dynamics in attention-deficit/hyperactivity disorder with fuzzy entropy. , 2015, Medical engineering & physics.

[2]  Jiang Wang,et al.  Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy. , 2015, Chaos.

[3]  T. Gasser,et al.  Alzheimer disease versus mixed dementias: An EEG perspective , 2008, Clinical Neurophysiology.

[4]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[5]  Roberto Hornero,et al.  Spatio-Temporal Fluctuations of Neural Dynamics in Mild Cognitive Impairment and Alzheimer's Disease. , 2017, Current Alzheimer research.

[6]  Roberto Hornero,et al.  Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[8]  P. Grassberger,et al.  Characterization of experimental (noisy) strange attractors , 1984 .

[9]  T Dierks,et al.  Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study , 2000, Clinical Neurophysiology.

[10]  J. Trojanowski,et al.  Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification , 2011, Neurobiology of Aging.

[11]  Stephen W. Scheff,et al.  Mild cognitive impairment: pathology and mechanisms , 2011, Acta Neuropathologica.

[12]  Paolo Massimo Buscema,et al.  An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features , 2015, Artif. Intell. Medicine.

[13]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[14]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[15]  A. Cichocki,et al.  Diagnosis of Alzheimer's disease from EEG signals: where are we standing? , 2010, Current Alzheimer research.

[16]  R. Schiffer,et al.  EEG Patterns in Mild Cognitive Impairment (MCI) Patients , 2008, The open neuroimaging journal.

[17]  M. Weiner,et al.  Neuroimaging markers for the prediction and early diagnosis of Alzheimer's disease dementia , 2011, Trends in Neurosciences.

[18]  Nick C Fox,et al.  The Diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease , 2011 .

[19]  R. Schiffer,et al.  Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG , 2001, Clinical Neurophysiology.

[20]  P. Neumann,et al.  The economics of mild cognitive impairment , 2013, Alzheimer's & Dementia.

[21]  Roberto Hornero,et al.  Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information , 2007, Comput. Methods Programs Biomed..

[22]  D. L. Hudson,et al.  Applying continuous chaotic modeling to cardiac signal analysis , 1996 .

[23]  Alzheimer’s Association 2017 Alzheimer's disease facts and figures , 2017, Alzheimer's & Dementia.

[24]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[25]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[26]  I. Grundke‐Iqbal,et al.  Tau pathology in Alzheimer disease and other tauopathies. , 2005, Biochimica et biophysica acta.

[27]  T. Ortiz,et al.  Complexity Analysis of Spontaneous Brain Activity in Alzheimer Disease and Mild Cognitive Impairment: An MEG Study , 2010, Alzheimer disease and associated disorders.

[28]  R. Hornero,et al.  Analysis of neural dynamics in mild cognitive impairment and Alzheimer's disease using wavelet turbulence , 2014, Journal of neural engineering.

[29]  Jyrki Ahveninen,et al.  Effects of scopolamine on MEG spectral power and coherence in elderly subjects , 2003, Clinical Neurophysiology.

[30]  Paolo Vitali,et al.  EEG spectral profile to stage Alzheimer's disease , 1999, Clinical Neurophysiology.

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

[32]  Roberto Hornero,et al.  Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients , 2008, Medical & Biological Engineering & Computing.

[33]  J. Gore,et al.  Mutual information analysis of the EEG in patients with Alzheimer's disease , 2001, Clinical Neurophysiology.

[34]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[35]  Placido Bramanti,et al.  Elman neural network for the early identification of cognitive impairment in Alzheimer's disease. , 2014, Functional neurology.

[36]  I. Percival,et al.  A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems , 1979 .

[37]  Roberto Hornero,et al.  Pattern recognition in airflow recordings to assist in the sleep apnoea–hypopnoea syndrome diagnosis , 2013, Medical & Biological Engineering & Computing.

[38]  Olivier J. J. Michel,et al.  Measuring time-Frequency information content using the Rényi entropies , 2001, IEEE Trans. Inf. Theory.

[39]  Huan Liu,et al.  Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..

[40]  Huan Liu,et al.  Redundancy based feature selection for microarray data , 2004, KDD.

[41]  D. Abásolo,et al.  Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease. , 2007, Medical engineering & physics.

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

[43]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[44]  Ronald C Petersen,et al.  Alzheimer's disease: progress in prediction , 2010, The Lancet Neurology.

[45]  Roberto Hornero,et al.  Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure. , 2006, Medical engineering & physics.

[46]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[47]  Claudio Babiloni,et al.  Individual analysis of EEG frequency and band power in mild Alzheimer's disease , 2004, Clinical Neurophysiology.

[48]  Richard J. Kryscio,et al.  Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease , 2014, Comput. Methods Programs Biomed..