Entropic Measures of EEG Complexity in Alzheimer's Disease Through a Multivariate Multiscale Approach

Alzheimer's disease (AD) impact is rapidly growing in western countries. The unavoidable progression of the disease, call for reliable ways to diagnose the AD in its early stages. Recently, it has been shown that the electroencephalography (EEG) complexity analysis could be used to predict the conversion from mild cognitive impairment (MCI) to AD. Despite the EEG analysis does not achieve yet the required clinical performance in terms of both sensitivity and specificity to be accepted as a clinically reliable technique of screening, the researchers count on the easiness and the non-invasiveness of the EEG measuring system. The aim of this paper is to analyze the efficacy of entropic complexity measures as a possible bio-marker to distinguish among the brain states related to the AD patients and MCI subjects from normal healthy elderly. The research is carried out on an experimental database. Three different emerging measures of complexity are compared, namely, permutation entropy, sample entropy, and Lempel-Ziv complexity. Because time series derived from biological systems show structures on multiple spatial-temporal scales and there exists a significant inter-channel correlation among the EEG channels, a multiscale multivariate approach is also implemented. Limited to the analyzed data, the results show that the severity of the AD reflects in the EEG dynamic complexity leaving the hope of early diagnosis based on simple EEG.

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

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  F. Boller,et al.  History of dementia and dementia in history: An overview , 1998, Journal of the Neurological Sciences.

[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]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[6]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

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

[8]  F. Collette,et al.  Alzheimer' Disease as a Disconnection Syndrome? , 2003, Neuropsychology Review.

[9]  M. Mattson Pathways towards and away from Alzheimer's disease , 2004, Nature.

[10]  M. Arif,et al.  Multiscale Permutation Entropy of Physiological Time Series , 2005, 2005 Pakistan Section Multitopic Conference.

[11]  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.

[12]  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.

[13]  D. Abásolo,et al.  Entropy analysis of the EEG background activity in Alzheimer's disease patients , 2006, Physiological measurement.

[14]  C. Peng,et al.  Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance–based therapy , 2007, Europhysics letters.

[15]  Soo Yong Kim,et al.  MULTISCALE ENTROPY ANALYSIS OF EEG FROM PATIENTS UNDER DIFFERENT PATHOLOGICAL CONDITIONS , 2007 .

[16]  Ning Xinbao Multiscale entropy analysis of complex physiologic time series , 2007 .

[17]  Francesco Carlo Morabito,et al.  Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings , 2007, 2007 International Joint Conference on Neural Networks.

[18]  J. Sleigh,et al.  Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. , 2008, British journal of anaesthesia.

[19]  R. Hornero,et al.  Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study , 2010, The open biomedical engineering journal.

[20]  Danilo P Mandic,et al.  Multivariate multiscale entropy: a tool for complexity analysis of multichannel data. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Andrzej Cichocki,et al.  Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin? , 2011, International journal of Alzheimer's disease.

[22]  Danilo P. Mandic,et al.  Multivariate Multiscale Entropy Analysis , 2012, IEEE Signal Processing Letters.

[23]  F. La Foresta,et al.  Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA , 2012, IEEE Sensors Journal.

[24]  A. Abbott Cognition: The brain's decline , 2012, Nature.

[25]  Francesco Carlo Morabito,et al.  Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG , 2012, Entropy.

[26]  D. Labate,et al.  Complexity Analysis of Alzheimer Disease EEG Data through Multiscale Permutation Entropy , 2012 .