Electroencephalography Classification of Healthy, Mild Cognitive Impairment and Probable Alzheimer’s Disease Through Linear and Non-Linear Biomarkers
Main Article Content
Abstract
Alzheimer’s disease is one of the main challenges of modern medicine since no cure has been found yet, the scientific community still does not fully understand the reasoning behind it, and any interventions found can delay the progress for only a limited amount of time. Over the years, research has shifted from attempts for curing the disease to efforts towards understanding the mechanisms behind it as well as finding tools that will speed up diagnosis many years before its clinical manifestations, when the brain deterioration begins. One of the many promising tools towards this direction is electroencephalography. Electroencephalography employs a variety of different measures that can be used as biomarkers for early diagnosis and differentiation of Alzheimer’s disease from other neurodegenerative disorders. Literature has produced a number of methods that have established reliable correlation between electroencephalography signals and structural abnormalities in Alzheimer’s disease. To that end, the present work proposes the combination of Tsallis Entropy and Higuchi Fractal Dimension within a common classification framework using machine learning techniques for classification among healthy, Mild Cognitive Impairment, and probable Alzheimer’s disease. The proposed methodology is applied on 75 subjects with different feature utilisation scenarios, reaching to an accuracy of 98.03% when classifying a signal epoch, following a 10-fold cross validation, as compared with other similar studies. Nevertheless, in a leave-one-out scenario with the same approach, the average accuracy drops significantly, suggesting that this method could complement other diagnosis approaches but cannot be used on each own.
Keywords:
Alzheimer’s disease, EEG, Tsallis Entropy, Higuchi Fractal Dimension, Signal Processing
Article Details
How to Cite
TSOLAKIS, Apostolos C. et al.
Electroencephalography Classification of Healthy, Mild Cognitive Impairment and Probable Alzheimer’s Disease Through Linear and Non-Linear Biomarkers.
Medical Research Archives, [S.l.], v. 10, n. 9, sep. 2022.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/3064>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.18103/mra.v10i9.3064.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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34. Garn H, Waser M, Deistler M, Benke T, Dal-Bianco P, Ransmayr G, Schmidt H, Sanin G, Santer P, Caravias G, Seiler S, Grossegger D, Fruehwirt W, Schmidt R. Quantitative EEG markers relate to Alzheimer’s disease severity in the Prospective Dementia Registry Austria (PRODEM). Clinical Neurophysiology, 2015;126(3):505-513.
35. Al-Nuaimi AH, Jammeh E, Sun L, Ifeachor E. Tsallis entropy as a biomarker for detection of Alzheimer's disease. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015:4166-4169.
36. Al-Nuaimi AHH, Jammeh E, Sun L, Ifeachor E. Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease. Complexity, 2018.
37. Tzimourta KD, Afrantou T, Ioannidis P, Karatzikoy M, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Glavas E, Grigoriadis N, Tsalikakis DG, Tsipouras MG. Analysis of electroencephalographic signals complexity regarding Alzheimer's Disease. Computers & Electrical Engineering, 2019;76:198-212.
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42. Accardo A, Affinito M, Carrozzi M, Bouquet F. Use of the fractal dimension for the analysis of electroencephalographic time series. Biological cybernetics, 1997;77(5):339-350.
43. Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena, 1988;31(2):277-283.
44. Henderson G, Ifeachor E, Hudson N, Goh C, Outram N, Wimalaratna S, Del Percio C, Vecchio F. Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Transactions on Biomedical Engineering, 2006;53(8):1557-1568.
45. Staudinger T, Polikar R. Analysis of complexity based EEG features for the diagnosis of Alzheimer's. Medicine and Biology Society, 2011:2033-2036).
46. Smits FM, Porcaro C, Cottone C, Cancelli A, Rossini PM, Tecchio F. Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease. PloS one, 2016;11(2):e0149587.
47. Al-Nuaimi AH, Jammeh E, Sun L, Ifeachor E. Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017:2320-2324.
48. Tsolakis AC, Petsos G, Kapetanou O, Nikolaidis IN, Aifantis, EC. Model analogies between pattern formation in deforming engineering materials & morphogenesis in ageing human brains. Journal of the Mechanical Behavior of Materials, 2019;28(1):95-106.
49. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research, 1975;12(3):189-198.
50. Kounti F, Tsolaki M, Kiosseoglou G. Functional cognitive assessment scale (FUCAS): a new scale to assess executive cognitive function in daily life activities in patients with dementia and mild cognitive impairment. Human Psychopharmacology: Clinical and Experimental, 2006;21(5):305-311.
51. Jasper HH. The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol., 1958;10:370-375.
52. Cukic M, Pokrajac D, Stokic M, Radivojevic V, Ljubisavljevic M. EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression. arXiv preprint arXiv:1803.05985, 2018.
53. Tian Y, Shi Y, Liu X. Recent advances on support vector machines research. Technological and economic development of Economy, 2012;18(1):5-33.
54. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016:785-794.
55. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 2017;30.
56. Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence. 2006:1015-1021.
57. Tharwat A. Classification assessment methods. Applied Computing and Informatics, 2021;17(1):168-192.
58. Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 2020;123:176-190.
2. Inside the brain. Alzheimer’s Association. https://www.alz.org/alzheimers-dementia/what-is-alzheimers/brain_tour_part_2. Accessed July 25, 2022
3. Kumar A, Singh A, Ekavali. A review on Alzheimer's disease pathophysiology and its management: an update. Pharmacological reports. 2015;67(2):195-203.
4. Petersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D, Gronseth GS, Marson D, Pringsheim T, Day GS, Sager M, Stevens J, Rae-Grant A. Practice guideline update summary: Mild cognitive impairment: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology, 2018;90(3):126-135.
5. McKhann GM, Knopman DS, Chertkow H, Hyman, BT, Jack Jr, CR, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. 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, 2011;7(3):263-269.
6. Blennow K, Zetterberg H. Biomarkers for Alzheimer's disease: current status and prospects for the future. Journal of Internal Medicine, 2018;284(6):643-663.
7. Weiner H, Schuster DB. The electroencephalogram in dementia. - Some preliminary observations and correlations. Electroencephalography and clinical neurophysiology, 1956;8(3):479-488.
8. Berger H. On the electroencephalogram of man. Twelfth report. Electroencephalography and clinical neurophysiology, 1969;267+.
9. Jeong J. EEG dynamics in patients with Alzheimer's disease. Clinical neurophysiology, 2004;115(7):1490-1505.
10. Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer's disease from EEG signals: where are we standing?. Current Alzheimer Research, 2010;7(6):487-505.
11. Alberdi A, Aztiria A, Basarab A. On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey. Artificial intelligence in medicine, 2016;71:1-29.
12. Houmani N, Vialatte F, Gallego-Jutglà E, Dreyfus G, Nguyen-Michel VH, Mariani J, Kinugawaet K. Diagnosis of Alzheimer’s disease with Electroencephalography in a differential framework. PloS one, 2018;13(3):e0193607.
13. Tsolaki A, Kazis D, Kompatsiaris I, Kosmidou V, Tsolaki M. Electroencephalogram and Alzheimer’s disease: clinical and research approaches. International journal of Alzheimer’s disease, 2014.
14. Jeong J. Nonlinear dynamics of EEG in Alzheimer's disease. Drug development research, 2002;56(2):57-66.
15. Woyshville MJ, Calabrese JR. Quantification of occipital EEG changes in Alzheimer's disease utilizing a new metric: the fractal dimension. Biological psychiatry, 1994;35(6):381-387.
16. Besthorn C, Zerfass R, Geiger-Kabisch C, Sattel H, Daniel S, Schreiter-Gasser U, Förstl H. Discrimination of Alzheimer's disease and normal aging by EEG data. Electroencephalography and Clinical Neurophysiology, 1997;103(2):241-248.
17. Jeong J, Kim SY, Han SH. Non-linear dynamical analysis of the eeg in alzheimer’s disease with optimal embedding dimension, Electroencephalography and clinical Neurophysiology, 1998;106:220–228.
18. 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. Clinical Neurophysiology, 1999;110(7):1159-1167.
19. Yagyu T, Wackermann J, Shigeta M, Jelic V, Kinoshita T, Kochi K, Julin P, Almkvist O, Wahlund LO, Kondakor I, Lehmann D. Global dimensional complexity of multichannel EEG in mild Alzheimer's disease and age-matched cohorts. Dementia and geriatric cognitive disorders, 1997;8(6):343-347.
20. Stam CJ, Jelles B, Achtereekte HAM, Rombouts, SARB, Slaets, JPJ, Keunen RWM. Investigation of EEG non-linearity in dementia and Parkinson's disease. Electroencephalography and clinical neurophysiology, 1995;95(5):309-317.
21. Yang S, Bornot JMS, Wong-Lin K, Prasad G. M/EEG-based bio-markers to predict the MCI and Alzheimer's disease: a review from the ML perspective. IEEE Transactions on Biomedical Engineering, 2019;66(10):2924-2935.
22. Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Transactions on Multimedia Computing, Communications, and Applications, 2020;16(1s):1-35.
23. Tsallis C. Possible generalization of Boltzmann-Gibbs statistics. Journal of statistical physics, 1988;52(1):479-487.
24. Gamero LG, Plastino A, Torres ME. Wavelet analysis and nonlinear dynamics in a nonextensive setting. Physica A: Statistical Mechanics and its Applications, 1997;246(3-4):487-509.
25. Capurro A, Diambra L, Lorenzo D, Macadar O, Martin MT, Mostaccio C, Plastino A, Pérez J, Rofman E, Torres ME, Velluti J. Human brain dynamics: the analysis of EEG signals with Tsallis information measure. Physica A: Statistical Mechanics and its Applications, 1999;265(1-2):235-254.
26. Martin MT, Plastino AR, Plastino A. Tsallis-like information measures and the analysis of complex signals. Physica A: Statistical Mechanics and its Applications, 2000;275(1-2):262-271.
27. Thakor NV, Paul J, Tong S, Zhu Y, Bezerianos A. Entropy of brain rhythms: normal versus injury EEG. In Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No. 01TH8563), 2001:261-264.
28. Thakor NV, Tong S. Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng., 2004;6:453-495.
29. Sneddon R. The Tsallis entropy of natural information. Physica A: Statistical Mechanics and its Applications, 2007;386(1):101-118.
30. Zhao P, Van-Eetvelt P, Goh C, Hudson N, Wimalaratna S, Ifeachor E. Characterization of EEGs in Alzheimer's disease using information theoretic methods. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007:5127-5131.
31. Zhao P, Van Eetvelt P, Goh C, Hudon N, Wimalaratna S, Ifeachor E. C. EEG markers of alzheimer’s disease using tsallis entropy. In Proceedings of the 3rd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007), Plymouth, UK, 2007;27.
32. De Bock TJ, Das S, Mohsin M, Munro NB, Hively LM, Jiang Y, Smith CD, Wekstein DR, Jicha GA, Lawson A, Lianekhammy J, Walsh E, Kiser S, Black C. Early detection of Alzheimer's disease using nonlinear analysis of EEG via Tsallis entropy. In 2010 Biomedical Sciences and Engineering Conference, 2010:1-4.
33. McBride J, Zhao X, Munro N, Jicha G, Smith C, Jiang Y. EEG multiscale entropy dynamics in mild cognitive impairment and early Alzheimer's disease. In Proceedings of the 2014 Biomedical Sciences and Engineering Conference, 2014:1-4.
34. Garn H, Waser M, Deistler M, Benke T, Dal-Bianco P, Ransmayr G, Schmidt H, Sanin G, Santer P, Caravias G, Seiler S, Grossegger D, Fruehwirt W, Schmidt R. Quantitative EEG markers relate to Alzheimer’s disease severity in the Prospective Dementia Registry Austria (PRODEM). Clinical Neurophysiology, 2015;126(3):505-513.
35. Al-Nuaimi AH, Jammeh E, Sun L, Ifeachor E. Tsallis entropy as a biomarker for detection of Alzheimer's disease. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015:4166-4169.
36. Al-Nuaimi AHH, Jammeh E, Sun L, Ifeachor E. Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease. Complexity, 2018.
37. Tzimourta KD, Afrantou T, Ioannidis P, Karatzikoy M, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Glavas E, Grigoriadis N, Tsalikakis DG, Tsipouras MG. Analysis of electroencephalographic signals complexity regarding Alzheimer's Disease. Computers & Electrical Engineering, 2019;76:198-212.
38. Campbell P, Abhyankar S. Fractals, form, chance and dimension, 1978.
39. Mandelbrot BB. The fractal geometry of nature/Revised and enlarged edition, New York, WH Free. Co, 1983;495:1.
40. Besthorn C, Sattel H, Geiger-Kabisch C, Zerfass R, Förstl H. Parameters of EEG dimensional complexity in Alzheimer's disease. Electroencephalography and clinical disease. In 2011 Annual International Conference of the IEEE Engineering in neurophysiology, 1995;95(2):84-89.
41. Pritchard WS, Duke DW. Measuring chaos in the brain-a tutorial review of EEG dimension estimation. Brain and cognition, 1995;27(3):353-397.
42. Accardo A, Affinito M, Carrozzi M, Bouquet F. Use of the fractal dimension for the analysis of electroencephalographic time series. Biological cybernetics, 1997;77(5):339-350.
43. Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena, 1988;31(2):277-283.
44. Henderson G, Ifeachor E, Hudson N, Goh C, Outram N, Wimalaratna S, Del Percio C, Vecchio F. Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Transactions on Biomedical Engineering, 2006;53(8):1557-1568.
45. Staudinger T, Polikar R. Analysis of complexity based EEG features for the diagnosis of Alzheimer's. Medicine and Biology Society, 2011:2033-2036).
46. Smits FM, Porcaro C, Cottone C, Cancelli A, Rossini PM, Tecchio F. Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease. PloS one, 2016;11(2):e0149587.
47. Al-Nuaimi AH, Jammeh E, Sun L, Ifeachor E. Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017:2320-2324.
48. Tsolakis AC, Petsos G, Kapetanou O, Nikolaidis IN, Aifantis, EC. Model analogies between pattern formation in deforming engineering materials & morphogenesis in ageing human brains. Journal of the Mechanical Behavior of Materials, 2019;28(1):95-106.
49. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research, 1975;12(3):189-198.
50. Kounti F, Tsolaki M, Kiosseoglou G. Functional cognitive assessment scale (FUCAS): a new scale to assess executive cognitive function in daily life activities in patients with dementia and mild cognitive impairment. Human Psychopharmacology: Clinical and Experimental, 2006;21(5):305-311.
51. Jasper HH. The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol., 1958;10:370-375.
52. Cukic M, Pokrajac D, Stokic M, Radivojevic V, Ljubisavljevic M. EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression. arXiv preprint arXiv:1803.05985, 2018.
53. Tian Y, Shi Y, Liu X. Recent advances on support vector machines research. Technological and economic development of Economy, 2012;18(1):5-33.
54. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016:785-794.
55. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 2017;30.
56. Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence. 2006:1015-1021.
57. Tharwat A. Classification assessment methods. Applied Computing and Informatics, 2021;17(1):168-192.
58. Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 2020;123:176-190.