Proposing an Approach for Diagnosis of Mild Cognitive Impairment Based on Approximate Entropy

Document Type : Original Article (s)

Authors

1 MSc Student, Department of Biomedical Engineering AND Student Research Committee, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

2 Associate Professor, Department of Physics and Medical Engineering, School of Medicine AND Department of Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

3 Professor, Department of Psychiatry AND Behavioral Sciences Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 PhD Student, Department of Biomedical Engineering AND Student Research Committee, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: The highly increase of Alzheimer's disease among human lead to increasing the demand of finding a reliable way to diagnose its symptoms at the early stages. Recent researches in this area demonstrate that the signal complexity analysis of the electroencephalogram can be useful in prognosis the development of this illness form mild cognitive impairment to Alzheimer's disease. The focus of this study was on approximate entropy and proposing an effective approach for using this criterion to diagnose the mild cognitive impairment.Methods: In this research, the electroencephalograms of 16 normal subjects and 11 patients were used. The signals were captured based on 10-20 international system for 30 minutes. In the preprocessing phase, the artefacts were eliminated both by visually inspection by a specialist physician and using band pass filter. In the processing phase, different scenarios were considered and applied to define the different parameters of approximate entropy. Finally, the results were analyzed using t-test to optimize the define protocol of the entropy and find the appropriate channels for diagnosing the disease.Findings: A protocol for extracting the complexity based on approximate entropy was determined, in which the difference of the entropy of normal subjects and patients were more remarkable. By using this protocol, the number of appropriate channels for diagnosing the disease increased (P < 0.05). These results also showed decreasing the gray matter volume in the patients with mild cognitive impairment.Conclusion: Using the entropy measurements for different channels of patients with mild cognitive impairment, demonstrate that the amount of complexity of signals decreased.

Keywords

References

  1. Prince M, Wimo A, Guerchet M, Ali G, Wu Y, Prina M. World Alzheimer Report 2015, The global impact of dementia: An analysis of prevalence, incidence, cost and trends. London, UK: Alzheimer's Disease International; 2015.
  2. Alzheimer's Association. 2016 Alzheimer's disease facts and figures. Alzheimers Dement 2016; 12(4): 459-509.
  3. Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer's disease from EEG signals: where are we standing? Curr Alzheimer Res 2010; 7(6): 487-505.
  4. Baker M, Akrofi K, Schiffer R, Boyle MW. EEG Patterns in Mild Cognitive Impairment (MCI) Patients. Open Neuroimag J 2008; 2: 52-5.
  5. Vialatte F, Cichocki A, Dreyfus G, Musha T, Rutkowski TM, Gervais R. Blind source separation and sparse bump modelling of time frequency representation of EEG signals: new tools for early detection of Alzheimer's disease. Proceedings of the 2005 IEEE Workshop on Machine Learning for Signal Processing; 2005 Sep 28-30; Mystic Hilton Mystic, CT, USA.
  6. Abasolo D, Hornero R, Espino P, Alvarez D, Poza J. Entropy analysis of the EEG background activity in Alzheimer's disease patients. Physiol Meas 2006; 27(3): 241-53.
  7. Abasolo D, Hornero R, Espino P, Poza J, Sanchez CI, de la Rosa R. Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy. Clin Neurophysiol 2005; 116(8): 1826-34.
  8. Dauwels J, Vialatte F, Musha T, Cichocki A. A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG. Neuroimage 2010; 49(1): 668-93.
  9. Subha DP, Joseph PK, Acharya UR, Lim CM. EEG signal analysis: a survey. J Med Syst 2010; 34(2): 195-212.
  10. Blachman N. The amount of information that y gives about X. IEEE Transactions on Information Theory 1968; 14(1): 27-31.
  11. Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 1991; 88(6): 2297-301.
  12. Bhattacharya J. Complexity analysis of spontaneous EEG. Acta Neurobiol Exp (Wars) 2000; 60(4): 495-501.
  13. Kermani S, Karimi Z, Barekatain M. Evaluation and estimation of gray matter volume using voxel-based morphometry of the brain magnetic resonance imaging (MRI) in normal elderly people and those with mild cognitive impairment. J Isfahan Med Sch 2015; 33(353): 1649-61. [In Persian].
  14. Karimi Z, Kermani S, Barekatain M. Diagnosis of mild cognitive impairment (MCI) via estimating the density of gray matter using voxel-based morphometry (VBM) in the brain magnetic resonance imaging (MRI). J Isfahan Med Sch 2016; 33(363): 2204-12. [In Persian].
  15. Whitwell JL, Petersen RC, Negash S, Weigand SD, Kantarci K, Ivnik RJ, et al. Patterns of atrophy differ among specific subtypes of mild cognitive impairment. Arch Neurol 2007; 64(8): 1130-8.
  16. Chetelat G, Landeau B, Eustache F, Mezenge F, Viader F, de la Sayette V, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage 2005; 27(4): 934-46.
  17. Zhenghui H, Pengcheng S. Regularity and complexity of human electroencephalogram dynamics: applications to diagnosis of Alzheimers disease. Proceedings of the 18th International Conference on Pattern Recognition; 2006 Aug 20-24; Hong Kong, China.
  18. Hornero R, Abasolo D, Escudero J, Gomez C. Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease. Philos Trans A Math Phys Eng Sci 2009; 367(1887): 317-36.
Journal of Isfahan Medical School
Volume 34, Issue 407 - Serial Number 407
September and October 2017
Pages 1356-1361

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History

  • Receive Date: 03 July 2016
  • Revise Date: 24 May 2024
  • Accept Date: 06 April 2022

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