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

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 analysis of variance with age as a covariate was 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.

[1]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

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

[3]  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.

[4]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[5]  Roberto Hornero,et al.  Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients. , 2006, Medical engineering & physics.

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

[7]  Tarmo Lipping,et al.  Comparison of entropy and complexity measures for the assessment of depth of sedation , 2006, IEEE Transactions on Biomedical Engineering.

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

[9]  Roberto Hornero,et al.  Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis , 2006, IEEE Transactions on Biomedical Engineering.

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

[11]  F Angeleri,et al.  EEG spectral analysis in vascular and Alzheimer dementia. , 1995, Electroencephalography and clinical neurophysiology.

[12]  Claudio Del Percio,et al.  Development and assessment of methods for detecting dementia using the human electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[13]  Erkki Oja,et al.  Independence: a new criterion for the analysis of the electromagnetic fields in the global brain? , 2000, Neural Networks.

[14]  J. Gotman,et al.  Isolation of epileptiform discharges from unaveraged EEG by independent component analysis , 1999, Clinical Neurophysiology.

[15]  D. Selkoe.  Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[16]  Roberto Hornero,et al.  Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[17]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[18]  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.

[19]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

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

[21]  Jen-Chuen Hsieh,et al.  Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis , 2007, Annals of Biomedical Engineering.

[22]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[23]  Roberto Hornero,et al.  Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation , 2008, Artif. Intell. Medicine.

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

[25]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

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

[27]  K H Ting,et al.  Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only. , 2006, Medical engineering & physics.

[28]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[29]  Roberto Hornero,et al.  Artifact Removal in Magnetoencephalogram Background Activity With Independent Component Analysis , 2007, IEEE Transactions on Biomedical Engineering.

[30]  A. J. Bell,et al.  INDEPENDENT COMPONENT ANALYSIS OF BIOMEDICAL SIGNALS , 2000 .

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

[32]  D A Steyn-Ross,et al.  Cortical entropy changes with general anaesthesia: theory and experiment. , 2004, Physiological measurement.

[33]  Dae-Jin Kim,et al.  Nonlinear dynamics of the EEG separated by independent component analysis after sound and light stimulation , 2002, Biological Cybernetics.

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

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