Skip to main content Skip to main navigation menu Skip to site footer

A Fast Screen System for Dementia Based on EEG Signals

  • Norden E. Huang
  • Wei-Shuai Yuan
  • Fang Yuan
  • Xiao-Min Guo
  • Albert CC Yang
  • Terry BJ Kuo
  • TieMei Zhang
  • Jian-Ping Cai
  • Helen Kang
  • Ying-Qiang Zhang
  • Wei-Kuang Liang

Abstract

We proposed a set of quantitative, inexpensive, and non-invasive methods for screening dementia based on resting EEG. The methods used here included ensemble Holo spectrum (eHolo), ensemble intrinsic Probability Density function (eiPDF), and ensemble intrinsic Multiscale Entropy (eiMSE). These methods were developed originally for physical sciences, specifically to study the wave-turbulence interaction phenomena. Applications, however, were found in neuroscience and bioengineering. Tested on mostly retrospective open-source EEG data, the results measured by receiver operating characteristic (ROC) curves and area under the curve (AUC) values based on micro- and macro-average methods all passed the clinically acceptable threshold of 70%. Specifically, the AUC scores are these: eHolo above 0.89, eiPDF at 0.82, and eiMSE above 0.87, respectively. If we use all three methods in combination, the AUC score is above 0.91. Emulating the bone-density measurement, we have also established a quantitative Z-score to measure the relative standing of each subject. Encouragingly, the Z-score shows a moderate correlation with the widely scattered CDR score, with RHO values between 0.26 and 0.46. We strongly suggest that large-scale clinical trials be organized so that the method can be validated and ready for the WHO and Chinese calls for screening of the general population by 2030.

Section

References

  1. 1. World Health Organization. Global action plan on the public health response to dementia 2017–2025 [Internet]. Geneva: The World Health Organization; 2017 [cited 2025 Apr 28]. Available from: https://www.who.int/publications/i/item/global-action-plan-on-the-public-health-response-to-dementia-2017---2025
  2. 2. Xiao J, Lia J, Wanga J, Zhangb X, Wang C, Pengd G, et al. Alzheimer's disease facts and figures. Alzheimers Dement. 2023;19(4):1598–1695. doi:10.1002/alz.13016
  3. 3. Chinese National Action Plan for Alzheimer’s Disease. 2024-2030) [Internet]. 2024 [cited 2025 Apr 28]. Available from: http://www.nhc.gov.cn/lljks/tggg/202501/2a01a6e45016496789370a276e8762f0.shtml
  4. 4. Gauthier S, Rosa-Neto P, Morais JA, Webster C. World Alzheimer Report 2021: Journey through the diagnosis of dementia [Internet]. London: Alzheimer’s Disease International; 2021 [cited 2025 Apr 28]. Available from: https://www.alzint.org/resource/world-alzheimer-report-2021
  5. 5. Gauthier S, Webster C, Servaes S, Morais JA, Rosa-Neto P. World Alzheimer Report 2022: Life after diagnosis: Navigating treatment, care and support [Internet]. London: Alzheimer’s Disease International; 2022 [cited 2025 Apr 28]. Available from: https://www.alzint.org/u/World-Alzheimer-Report-2022.pdf
  6. 6. Liu Y, et al. Projection for dementia burden in China to 2050: A macro-simulation study by scenarios of dementia incidence trends. Lancet Reg Health West Pac. 2024;50:101158. doi:10.1016/j.lanwpc.2021.101158
  7. 7. Rosa-Neto P. Differential diagnosis. In: Gauthier S, Rosa-Neto P, Morais JA, Webster C, editors. World Alzheimer Report 2021: Journey through the diagnosis of dementia. London: Alzheimer’s Disease International; 2021. Chapter 14. Available from: https://www.alzint.org/resource/world-alzheimer-report-2021
  8. 8. Brenowitz WD, Hubbard RA, Keene CD, Hawes SE, Longstreth WT Jr, Woltjer RL, et al. Mixed neuropathologies and estimated rates of clinical progression in a large autopsy sample. Alzheimers Dement. 2017;13(6):654–62. doi:10.1016/j.jalz.2016.09.015
  9. 9. Wang X, Zhu K, Wu W, Zhou D, Lu H, Du J, et al. Prevalence of mixed neuropathologies in age-related neurodegenerative diseases: A community-based autopsy study in China. Alzheimers Dement. 2025;21(1):e14369. doi:10.1002/alz.14369
  10. 10. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology. 2007;69(24):2197–204. doi:10.1212/01.wnl.0000271090.28148.24
  11. 11. Kapasi A, DeCarli C, Schneider JA. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol. 2017;134(2):171–86. doi:10.1007/s00401-017-1717-7
  12. 12. Tanaka M, Yamada E, Mori F. Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia. Front Aging Neurosci. 2024;16:1486481.
  13. 13. Nayana BR, Pavithra MN, Chaitra S, Bhuvana Mohini TN, Stephan T, Mohan V, et al. EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models. Sci Rep. 2025;15(1):15950.
  14. 14. Chetty CA, Bhardwaj H, Kumar GP, et al. EEG biomarkers in Alzheimer’s and prodromal Alzheimer’s: a comprehensive analysis of spectral and connectivity features. Alz Res Therapy. 2024;16:236. doi:10.1186/s13195-024-01582-w. 2025;Preprint:13872877251327754.
  15. 15. Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Flores-Sandoval AA, et al. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. Neurobiol Dis. 2024;190:106380.
  16. 16. Hu D, Chen M, Li X, Daley S, Morin P, Han Y, et al. Unlocking the potential of EEG in Alzheimer's disease research. Clin Neurophysiol. 2025;136:93–102.
  17. 17. Huang NE, Yuan W, Yang ACC, Kuo TBJ, Tang WX, Kang H, et al. Quantifying consciousness through the intrinsic Probability Density Function. Manuscript in preparation.
  18. 18. Miltiadous A, Tzimourta KD, Afrantou T, Ioannidis P, Grigoriadis N, Tsalikakis DG, et al. A dataset of scalp EEG recordings of Alzheimer’s disease, frontotemporal dementia and healthy subjects from routine EEG. Data. 2023;8(6):95. doi:10.3390/data8060095
  19. 19. Babayan A, Erbey M, Kumral D, Reinelt JD, Reiter AMF, Röbbig J, et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data. 2019;6:180308. doi:10.1038/sdata.2018.308
  20. 20. Kim MJ, Youn YC, Paik J. Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset. Neuroimage. 2023;272:120054. doi:10.1016/j.neuroimage.2023.120054
  21. 21. Fatnan MH, Hussain Z. Blind source separation under semi-white Gaussian noise and uniform noise: Performance analysis of ICA, SOBI, and JadeR. J Comput Sci. 2019;15(1):27–44. doi:10.3844/jcssp.2019.27.44
  22. 22. Winkler I, Haufe S, Tangermann M. Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav Brain Funct. 2011;7(1):30. doi:10.1186/1744-9081-7-30
  23. 23. Winkler I, Brandl S, Horn F, Waldburger E, Allefeld C, Tangermann M. Robust artifactual independent component classification for BCI practitioners. J Neural Eng. 2014;11(3):035013. doi:10.1088/1741-2560/11/3/035013
  24. 24. Gabard-Durnam LJ, Mendez Leal AS, Wilkinson CL, Levin AR. The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized processing software for developmental and high-artifact data. Front Neurosci. 2018;12:97. doi:10.3389/fnins.2018.00097
  25. 25. Huang NE, Yuan W, Wu Z, Qiao F, Jiang WZ, Kang H, et al. On an intrinsic probability distribution function for time series data. Manuscript in preparation.
  26. 26. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci. 1998;454(1971):903–95. doi:10.1098/rspa.1998.0193
  27. 27. Wu Z, Huang NE, Long SR, Peng CK. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci U S A. 2007;104(38):14889–94. doi:10.1073/pnas.0701020104
  28. 28. Wu Z, Huang NE. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal. 2009;1(1):1–41. doi:10.1142/S1793536909000042
  29. 29. Huang NE, Hu K, Yang ACC, Chang HC, Jia D, Liang WK, et al. On Holo-Hilbert spectral analysis: A full informational spectral representation for nonlinear and non-stationary data. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150206. doi:10.1098/rsta.2015.0206
  30. 30. Huang NE, Wu Z, Long SR, Arnold KC, Chen X, Blank K. On instantaneous frequency. Adv Adapt Data Anal. 2009;1(2):177–229. doi:10.1142/S1793536909000096
  31. 31. Keshmiri S. Entropy and the brain: An overview. Entropy. 2020;22(9):917. doi:10.3390/e22090917
  32. 32. Shalbaf R, Behnam H, Sleigh JW, Steyn-Ross A, Voss LJ. Monitoring the depth of anesthesia using entropy features and an artificial neural network. J Neurosci Methods. 2013;218(1):17–24. doi:10.1016/j.jneumeth.2013.03.008
  33. 33. Su C, Liang Z, Li X, Li D, Li Y, Ursino M. A comparison of multiscale permutation entropy measures in online depth of anesthesia monitoring. PLoS One. 2016;11(10):e0164104. doi:10.1371/journal.pone.0164104
  34. 34. Liu Q, Chen YF, Fan SZ, et al. EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery. Med Biol Eng Comput. 2017;55:1435–50. doi:10.1007/s11517-016-1598-2
  35. 35. Hogan MJ, Kilmartin L, Keane M, et al. Electrophysiological entropy in younger adults, older controls, and older cognitively declined adults. Brain Res. 2012;1445:1–10. doi:10.1016/j.brainres.2012.01.028
  36. 36. Schätz M, Vyšata O, Kopal J, Procházka A. Comparison of complexity, entropy and complex noise parameters in EEG for AD diagnosis. J Neurol Sci. 2013;333:e355. doi:10.1016/j.jns.2013.07.1303
  37. 37. Houmani N, Dreyfus G, Vialatte FB. Epoch-based entropy for early screening of Alzheimer’s disease. Int J Neural Syst. 2015;25(4):1550032. doi:10.1142/S0129065715500327
  38. 38. Ruiz-Gómez S, Gómez C, Poza J, Gutiérrez-Tobal G, Tola-Arribas M, Cano M, et al. Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy. 2018;20(1):35. doi:10.3390/e20010035
  39. 39. Niu Y, Wang B, Zhou M, et al. Dynamic complexity of spontaneous bold activity in Alzheimer’s disease and mild cognitive impairment using multiscale entropy analysis. Front Neurosci. 2018;12:677. doi:10.3389/fnins.2018.00677
  40. 40. Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG dynamics for early diagnosis of Alzheimer's disease using permutation entropy. Neuromarker. Comput Methods Programs Biomed. 2021;206:106116. doi:10.1016/j.cmpb.2021.106116
  41. 41. Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A review of methods of diagnosis and complexity analysis of Alzheimer's disease using EEG signals. Biomed Res Int. 2021;2021:5425569. doi:10.1155/2021/5425569
  42. 42. Aviles M, Sánchez-Reyes LM, Álvarez-Alvarado JM, Rodríguez-Reséndiz J. Machine and deep learning trends in EEG-based detection and diagnosis of Alzheimer’s disease: A systematic review. Eng. 2024;5(3):1464–84. doi:10.3390/eng5030078
  43. 43. Burioka N, Miyata M, Cornélissen G, Halberg F, Takeshima T, Kaplan DT, et al. Approximate entropy in the electroencephalogram during wake and sleep. Clin EEG Neurosci. 2005;36(1):21–4. doi:10.1177/155005940503600106
  44. 44. Shi W, Feng H, Zhang X, Yeh CH. Amplitude modulation multiscale entropy characterizes complexity and brain states. Chaos Solitons Fractals. 2023;173:113646. doi:10.1016/j.chaos.2023.113646
  45. 45. Miskovic V, MacDonald KJ, Rhodes LJ, Cote KA. Changes in EEG multiscale entropy and power-law frequency scaling during the human sleep cycle. Hum Brain Mapp. 2019;40(2):538–51. doi:10.1002/hbm.24393
  46. 46. Delgado-Bonal A, Marshak A. Approximate entropy and sample entropy: A comprehensive tutorial. Entropy. 2019;21(6):541. doi:10.3390/e21060541
  47. 47. Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of physiologic time series. Phys Rev Lett. 2002;89(6):062102. doi:10.1103/PhysRevLett.89.062102
  48. 48. Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E. 2005;71(2):021906. doi:10.1103/PhysRevE.71.021906
  49. 49. Yeh JR, Peng CK, Huang NE. Scale-dependent intrinsic entropies of complex time series. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150204. doi:10.1098/rsta.2015.0204
  50. 50. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: A highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30.
  51. 51. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765–74.
  52. 52. Bi X, Wang H. Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning. Neural Netw. 2019;114:119–35. doi:10.1016/j.neunet.2019.02.005
  53. 53. Ieracitano C, Mammone N, Bramanti A, Hussain A, Morabito FC. A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing. 2019;323:96–107. doi:10.1016/j.neucom.2018.09.071
  54. 54. 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 Netw. 2020;123:176–90. doi:10.1016/j.neunet.2019.12.006
  55. 55. Sharma N, Kolekar MH, Jha K, Kumar Y. EEG and cognitive biomarkers based mild cognitive impairment diagnosis. IRBM. 2019;40(2):113–21. doi:10.1016/j.irbm.2018.11.007
  56. 56. Zheng X, Wang B, Liu H, Wu W, Sun J, Fang W, et al. Diagnosis of Alzheimer’s disease via resting-state EEG: Integration of spectrum, complexity, and synchronization signal features. Front Aging Neurosci. 2023;15:1288295. doi:10.3389/fnagi.2023.1288295
  57. 57. Chu KT, Lei WC, Wu MH, Fuh JL, Wang SJ, French IT, et al. A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer’s disease. Front Aging Neurosci. 2023;15:1195424. doi:10.3389/fnagi.2023.1195424
  58. 58. Wang R, He Q, Shi L, et al. Automatic detection of Alzheimer’s disease from EEG signals using an improved AFS–GA hybrid algorithm. Cogn Neurodyn. 2024;18:2993–3013. doi:10.1007/s11571-024-10130-z
  59. 59. Jack CR Jr, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, et al. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement. 2024;20(8):5143–69. doi:10.1002/alz.13859
  60. 60. United Nations, Department of Economic and Social Affairs, Population Division. World population prospects 2024 [Internet]. 2024 [cited 2025 Apr 28]. Available from: https://population.un.org/wpp/
  61. 61. Arevalo-Rodriguez I, Smailagic N, Roqué-Figuls M, Ciapponi A, Sanchez-Perez E, Giannakou A, et al. Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2021;7(7):CD010783. doi:10.1002/14651858.CD010783.pub3
  62. 62. Lin CC. EEG manifestations in metabolic encephalopathy. Acta Neurol Taiwan. 2005;14(3):151-161.

How to Cite

“A Fast Screen System for Dementia Based on EEG Signals”. Human Brain, vol. 4, no. 1, June 2025, https://doi.org/10.37819/hb.1.2068.

How to Cite

“A Fast Screen System for Dementia Based on EEG Signals”. Human Brain, vol. 4, no. 1, June 2025, https://doi.org/10.37819/hb.1.2068.

HTML
23

Total
37

Citations
undefined

Share

Downloads

Article Details

Most Read This Month

License

Copyright (c) 2025 Norden E. Huang, Wei-Shuai Yuan, Fang Yuan, Xiao-Min Guo, Albert CC Yang , Terry BJ Kuo , TieMei Zhang, Jian-Ping Cai, Helen Kang, Ying-Qiang Zhang , Wei-Kuang Liang

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.