Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease

Many clinical studies have shown that electroencephalograms (EEG) of Alzheimer patients (AD) often have an abnormal power spectrum. In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD from EEG signals. Relative power in different EEG frequency bands is used as features to distinguish between AD patients and healthy control subjects. Many different frequency bands between 4 and 30Hz are systematically tested, besides the traditional frequency bands, e.g., theta band (4–8Hz). The discriminative power of the resulting spectral features is assessed through statistical tests (Mann-Whitney U test). Moreover, linear discriminant analysis is conducted with those spectral features. The optimized frequency ranges (4–7Hz, 8–15Hz, 19–24Hz) yield substantially better classification performance than the traditional frequency bands (4–8Hz, 8–12Hz, 12–30Hz); the frequency band 4–7Hz is the optimal frequency range for detecting AD, which is similar to the classical theta band. The frequency bands were also optimized as features through leave-one-out crossvalidation, resulting in error-free classification. The optimized frequency bands may improve existing EEG based diagnostic tools for AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

[1]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[2]  Andrzej Cichocki,et al.  EEG Classification of Mild and Severe Alzheimer's Disease Using Parallel Factor Analysis Method , 2008, World Congress on Engineering.

[3]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[4]  N Monson,et al.  Decreased alpha bandwidth responsiveness to photic driving in Alzheimer disease. , 1992, Electroencephalography and clinical neurophysiology.

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

[6]  M. Storandt,et al.  A longitudinal EEG study of mild senile dementia of Alzheimer type: changes at 1 year and at 2.5 years. , 1985, Electroencephalography and clinical neurophysiology.

[7]  Andrzej Cichocki,et al.  Improving the Quality of EEG Data in Patients with Alzheimer's Disease Using ICA , 2009, ICONIP.

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

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

[10]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[11]  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.

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

[13]  J. G. van Dijk,et al.  EEG and MRI correlates of mild cognitive impairment and Alzheimer's disease , 2007, Neurobiology of Aging.

[14]  P. Nunez,et al.  Electric fields of the brain , 1981 .

[15]  G. Schumock,et al.  Economic Considerations in Alzheimer's Disease , 1998, Pharmacotherapy.

[16]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[17]  R. Gervais,et al.  Blind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer's Disease , 2022 .

[18]  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.

[19]  Robert P. W. Duin,et al.  Health Monitoring with Learning Methods , 2001, ICANN.

[20]  C. Bigan,et al.  P04.3 Characterisation of EEG at different stages of Alzheimer’s disease (AD) , 2006, Clinical Neurophysiology.

[21]  Cristin Bigan,et al.  Characterization of EEG at different Stages of Alzheimer's disease , 2006 .

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

[23]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[24]  Chris Gilleard,et al.  DIAGNOSIS OF ALZHEIMERS-DISEASE , 1991 .

[25]  Pramod K. Varshney,et al.  Quantifying EEG synchrony using copulas , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  Christoph Lehmann,et al.  Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.

[27]  H. Adeli,et al.  Fractality and a Wavelet-chaos-Methodology for EEG-based Diagnosis of Alzheimer Disease , 2011, Alzheimer disease and associated disorders.

[28]  Andrzej Cichocki,et al.  EEG synchrony analysis for early diagnosis of Alzheimer's disease: A study with several synchrony measures and EEG data sets , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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