Evaluation of Electroencephalography Signals in Alzheimer’s Disease Using Coherence Analysis and Persistent Homology

Year 2025, Volume: 15 Issue: 2, 127 - 134, 29.08.2025

Mustafa Bayrak , Ömer Bahadır Eryılmaz , Cihan Katar , Atilla Uslu

https://doi.org/10.26650/experimed.1657631

Abstract

Objective: This study aimed to use a new approach, namely persistent homology, to analyse electroen cephalogram (EEG) coherence and identify the alterations in brain connectivity in patients with Alzheimer’s disease (AD).

Materials and Methods: We applied persistent homology to the distance maps that we created using the EEG coherence values from five different frequency bands in order to determine if there are disruptions specific to these bands in patients diagnosed with AD.

Results: Our findings revealed that the features extracted using persistent homology were significantly different in two bands (delta and theta) between AD patients and subjects in the healthy control (HC) group. Furthermore, the machine learning algorithms using these topological features achieved accurate classification results. This suggests that persistent homology may be a useful adjunct in the diagnosis of AD.

Conclusion: We have demonstrated the potential of persistent homology in identifying AD-related changes in brain connectivity, which are the most clearly present in the theta and delta bands. Larger datasets should be used in future research to determine the clinical relevancy of this method.

Keywords

Alzheimer’s disease , EEG , Coherence , Persistent homology

References

• 1. Ulep MG, Saraon SK, McLea S. Alzheimer disease. J Nurse Pract 2018; 14(3): 129-35. google scholar
• 2. Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E. Alzheimer's disease. Lancet 2011; 377(9770): 1019-31. google scholar
• 3. Harwood DG, Sultzer DL, Wheatley MV. Impaired insight in Alzheimer disease: association with cognitive deficits, psychiatric symptoms, and behavioral disturbances. Cogn Behav Neurol 2000; 13(2): 83-8. google scholar
• 4. Khachaturian ZS. Diagnosis of Alzheimer's disease. Archives Neurol 1985; 42(11): 1097-105. google scholar
• 5. Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment. Dis Markers 2018; 2018: 5174815. google scholar
• 6. Kulkarni N, Bairagi VK. Diagnosis of Alzheimer disease using EEG signals. Int J Eng Res 2014; 3(4): 1835-8. google scholar
• 7. Gawel M, Zalewska E, Szmidt-Sałkowska E, Kowalski J. The value of quantitative EEG in differential diagnosis of Alzheimer’s disease and subcortical vascular dementia. J Neurol Sci 2009; 283(1-2): 127–33. google scholar
• 8. Garn H, Waser M, Deistler M, Schmidt R, Dal-Bianco P, Ransmayr G, et al. Quantitative EEG in Alzheimer’s disease: Cognitive state, resting state and association with disease severity. Int J Psychophysiol 2014; 93(3): 390–7. google scholar
• 9. Yener GG, Leuchter AF, Jenden D, Read SL, Cummings JL, Miller BL. Quantitative EEG in frontotemporal dementia. Clin Electroencephalogr 1996; 27(2): 61-8. google scholar
• 10. Jelic V, Shigeta M, Julin P, Almkvist O, Winblad B, Wahlund LO. Quantitative electroencephalography power and coherence in Alzheimer's disease and mild cognitive impairment. Dement Geriatr Cogn Disord 1996; 7(6): 314-23. google scholar
• 11. 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. google scholar
• 12. Lizio R, Vecchio F, Frisoni GB, Ferri R, Rodriguez G, Babiloni C. Electroencephalographic rhythms in Alzheimer′s disease. Int J Alzheimer’s Dis 2011; 2011: 927573. google scholar
• 13. Fischer MH, Zibrandtsen IC, Høgh P, Musaeus CS. Systematic review of EEG coherence in Alzheimer’s disease. J Alzheimers Dis 2023; 91(4): 1261-72. google scholar
• 14. Kowalski JW, Gawel M, Pfeffer A, Barcikowska M. The diagnostic value of EEG in Alzheimer disease: correlation with the severity of mental impairment. Clin Neurophysiol 2001; 18(6): 570-5. google scholar
• 15. Onishi J, Suzuki Y, Yoshiko K, Hibino S, Iguchi A. Predictive model for assessing cognitive impairment by quantitative electroencephalography. Cogn Behav Neurol 2005; 18(3): 179-84. google scholar
• 16. Van der Hiele K, Vein AA, Reijntjes RH, Westendorp RG, Bollen EL, Van Buchem MA, et al. EEG correlates in the spectrum of cognitive decline. Clin Neurophysiol 2007; 118(9): 1931-9. google scholar
• 17. French CC, Beaumont JG. A critical review of EEG coherence studies of hemisphere function. Int J Psychophysiol 1984; 1(3): 241-54. google scholar
• 18. Carlsson G. Topology and data. Bull Am Math Soc 2009; 46(2): 255-308. google scholar
• 19. Yamanashi T, Kajitani M, Iwata M, Crutchley KJ, Marra P, Malicoat JR, et al. Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium. Sci Rep 2021; 11(1): 304. google scholar
• 20. Cai T, Zhao G, Zang J, Zong C, Zhang Z, Xue C. Topological feature search method for multichannel EEG: Application in ADHD classification. Biomed Signal Process Control 2025; 100: 107153. google scholar
• 21. Sathyanarayana A, Manjunath S, Perea JA. Topological data analysis based characteristics of electroencephalogram signals in children with sleep apnea. J Sleep Res 2025: e70017. google scholar
• 22. Piangerelli M, Rucco M, Tesei L, Merelli E. Topological classifier for detecting the emergence of epileptic seizures. BMC Res Notes 2018; 11: 1-7. google scholar
• 23. Rutkowski TM, Abe MS, Sugimoto H, Otake-Matsuura M. Mild cognitive impairment detection with machine learning and topological data analysis applied to EEG time-series in facial emotion oddball paradigm. In2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2023 (pp. 1-4). IEEE. google scholar
• 24. 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. google scholar
• 25. Abuhassan K, Coyle D, Belatreche A, Maguire L. Compensating for synaptic loss in Alzheimer’s disease. J Comput Neurosci 2014; 36: 19-37. google scholar
• 26. Fonseca LC, Tedrus GMAS, Prandi LR, Almeida AM, Furlanetto DS. Alzheimer’s disease: Relationship between cognitive aspects and power and coherence EEG measures. Arq Neuro-Psiquiatr 2011; 69(6): 875–81. google scholar
• 27. Musaeus CS, Engedal K, Høgh P, Jelic V, Mørup M, Naik M, et al. Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease. Clin Neurophysiol 2019; 130(10): 1889-99. google scholar
• 28. Duan F, Huang Z, Sun Z, Zhang Y, Zhao Q, Cichocki A, et al. Topological network analysis of early Alzheimer’s disease based on resting-state EEG. IEEE Trans Neural Syst Rehabil Eng 2020; 28(10): 2164-72. google scholar
• 29. Fan M, Yang AC, Fuh JL, Chou CA. Topological pattern recognition of severe Alzheimer's disease via regularized supervised learning of EEG complexity. Front Neurosci 2018; 12: 685. google scholar