Non-linear Analysis of the Electroencephalogram in Alzheimer’s Disease by Means of Symbolic Sequence Decomposition Method

Year 2015, Volume: 3 Issue: 1, 14 - 17, 17.01.2015

Pinar Tosun

https://doi.org/10.18100/ijamec.51421

Cited By: 1

Abstract

In this pilot study, a symbolic sequence decomposition method was used in conjunction with Shannon’s entropy to investigate the changes in electroencephalogram signals of 11 patients with Alzheimer’s disease and 11 age-matched control subjects. Results were statistically analysed by student t-test and later classified with receiver operating curves. Statistically significant differences between both groups were found at electrodes Fp1, O2, P3, T4 and T5. Sensitivity (defined as percentages of correctly classified patients) and specificity (defined as correctly classified controls) were evaluated using the receiver operating curves method. Accuracy of the methods was calculated according to sensitivity and specificity measures of electrodes showing statistically significant differences between the control group and Alzheimer’s disease patients and ranged between 72.73-77.27%. These accuracy values were in agreement with previously published entropy studies on this data set. Although combining these methods did not provide any greater accuracy over previous findings, using a symbolic sequence decomposition method enhanced the data processing.

Keywords

Alzheimer’s Disease, Electroencephalogram, Non-linear Analysis, Symbolic Sequence Decomposition, Shannon’s Entropy

Non-linear Analysis of the Electroencephalogram in Alzheimer’s Disease by Means of Symbolic Sequence Decomposition Method

Abstract

References

• M. Graeber, S. Kösel, R. Egensperger, R. Banati, U. Müller, K. Bise, P. Hoff, H. Möller, K. Fujisawa and P. Mehraein. Rediscovery of the case described by alois alzheimer in 1911: Historical, histological and molecular genetic analysis. Neurogenetics 1(1), pp. 73-80. 1997.
• G. M. McKhann, D. S. Knopman, H. Chertkow, B. T. Hyman, C. R. Jack, C. H. Kawas, W. E. Klunk, W. J. Koroshetz, J. J. Manly, R. Mayeux, R. C. Mohs, J. C. Morris, M. N. Rossor, P. Scheltens, M. C. Carrillo, B. Thies, S. Weintraub and C. H. Phelps. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for alzheimer's disease Alzheimer's & Dementia 7(3), pp. 263-269. 2011.
• D. S. Knopman, S. T. DeKosky, J. L. Cummings, H. Chui, J. Corey-Bloom, N. Relkin, G. W. Small, B. Miller and J. C. Stevens. Practice parameter: Diagnosis of dementia (an evidence-based review). report of the quality standards subcommittee of the american academy of neurology. Neurology 56(9), pp. 1143-1153. 2001.
• B. Reisberg, R. Doody, A. Stöffler, F. Schmitt, S. Ferris and H. J. Möbius. Memantine in moderate-to-severe alzheimer's disease. N. Engl. J. Med. 348(14), pp. 1333 1341. 2003.
• J. Jeong. EEG dynamics in patients with alzheimer's disease. Clinical Neurophysiology 115(7), pp. 1490-1505. 2004.
• D. Abásolo, R. Hornero, P. Espino, D. Alvarez and J. Poza. Entropy analysis of the EEG background activity in alzheimer's disease patients. Physiol. Meas. 27(3), pp. 241. 2006.
• D. Abásolo, R. Hornero, P. Espino, J. Poza, C. I. Sánchez and R. de la Rosa. Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy. Clinical Neurophysiology 116(8), pp. 1826-1834. 2005.
• D. Abásolo, R. Hornero, C. Gómez, M. García and M. López. Analysis of EEG background activity in Alzheimer’s disease patients with Lempel–Ziv complexity and central tendency measure. Med. Eng. Phys. 28(4), pp. 315-322. 2006.
• C. J. Stam. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology 116(10), pp. 2266-2301. 2005.
• Jelles B, van Birgelen JH, Slaets JPJ, Hekster REM, Jonkman EJ, Stam CJ. Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls. Clin Neurophysiology (110), pp.1159–1167. 1999.
• Jeong J, Kim DJ, Chae JH, Kim SY, Ko HJ, Paik IH. Nonlinear analysis of the EEG of schizophrenics with optimal embedding dimension. Med Eng Phys (20), pp. 669–676. 1998.
• K. Keller and H. Lauffer. Symbolic analysis of high-dimensional time series. International Journal of Bifurcation and Chaos 13(09), pp. 2657-2668. 2003.
• A. Abbott. Sequence analysis: New methods for old ideas. Annual Review of Sociology 21(1), pp. 93-113. 1995.
• C. S. Daw, C. E. A. Finney and E. R. Tracy. A review of symbolic analysis of experimental data. Rev. Sci. Instrum. 74(2), pp. 915-930. 2003.
• S. Tong and N. V. Thakor. Advances in quantitative electroencephalogram analysis methods. Annual Review of Biomedical Engineering. 6(01). pp.453-456. 2004.
• P. E. Rapp. A guide to dynamical analysis. Integrative Physiological and Behavioural Science. 29(3). pp. 311-317. 1994.
• L. Kurlowicz and M. Wallace. The Mini Mental State Examination (MMSE). The Hartford Institute of Geriatric Nursing. Issue 3. 1999.
• C. E. Shannon. A mathematical theory of communication. The Bell Technical Journal. 27. pp. 379-423. 1948.
• J. Bruhn, L. E. Röpcke, H. Bouillon and A. Hoeft. Shannon entropy applied to the measurement of the electroencephalographic effects of desflurane. Anesthesiology. 95(1). pp. 30-35. 2001.
• J. W. Sleigh, D. A. Steyn-Ross, M. L. Steyn-Ross, C. Grant and G. Ludbook. Cortical entropy changes with general anaesthesia: theory and experiment. Phsyiological Measurement. 25(4). pp. 921-927. 2004
• T. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters. 27. pp. 861-874. 2006.