World Scientific
  • Search
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

Supporting the Detection of Early Alzheimer’s Disease with a Four-Channel EEG Analysis

    https://doi.org/10.1142/S0129065723500211Cited by:6 (Source: Crossref)

    Alzheimer’s disease (AD) is the most prevalent form of dementia. Although there is no current cure, medical treatment can help to control its progression. Hence, early-stage diagnosis is crucial to maximize the living standards of the patients. Biochemical markers and medical imaging in combination with neuropsychological tests represent the most extended diagnosis procedure. However, these techniques require specialized personnel and long processing time. Furthermore, the access to some of these techniques is often limited in crowded healthcare systems and rural areas. In this context, electroencephalography (EEG), a non-invasive technique to obtain endogenous brain information, has been proposed for the diagnosis of early-stage AD. Despite the valuable information provided by clinical EEG and high density montages, these approaches are impractical in conditions such as those described above. Consequently, in this study, we evaluated the feasibly of using a reduced EEG montage with only four channels to detect early-stage AD. For this purpose, we involved eight clinically diagnosed AD patients and eight healthy controls. The results we obtained reveal similar accuracies (p-value=0.66) for the reduced montage (0.86) and a 16-channel montage (0.87). This suggests that a four-channel wearable EEG system could be an effective tool for supporting early-stage AD detection.

    References

    • 1. C. Patterson, World Alzheimer Report 2018, https://apo.org.au/node/260056 (2018). Google Scholar
    • 2. E. Perez-Valero, J. Minguillon, C. Morillas, F. Pelayo and M. A. Lopez-Gordo, Detection of Alzheimer’s disease using a four-channel EEG Montage, in Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, eds. J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. de la Paz López and H. Adeli (Springer International Publishing, New York, 2022), pp. 436–445, https://doi.org/10.1007/978-3-031-06242-1. Google Scholar
    • 3. S. Palmqvist et al., Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease, Neurology 85 (2015) 1240–1249. Crossref, Medline, Web of ScienceGoogle Scholar
    • 4. G. S. Bloom, Amyloid-β and Tau: The trigger and bullet in alzheimer disease pathogenesis, JAMA Neurol. 71 (2014) 505–508. Crossref, Medline, Web of ScienceGoogle Scholar
    • 5. J. Huang, P. Beach, A. Bozoki and D. C. Zhu, Alzheimer’s disease progressively reduces visual functional network connectivity, J. Alzheimer’s Dis. Rep. 5 (2021) 549–562. Crossref, MedlineGoogle Scholar
    • 6. M. Zvěřová, Clinical aspects of Alzheimer’s disease, Clin. Biochem. 72 (2019) 3–6. Crossref, Medline, Web of ScienceGoogle Scholar
    • 7. R. J. Perrin, A. M. Fagan and D. M. Holtzman, Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease, Nature 461 (2009) 916–922. Crossref, Medline, Web of ScienceGoogle Scholar
    • 8. M. Riemenschneider et al., Cerebrospinal fluid tau and beta-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment, Arch. Neurol. 59 (2002) 1729–1734. Crossref, MedlineGoogle Scholar
    • 9. H. Zetterberg, L.-O. Wahlund and K. Blennow, Cerebrospinal fluid markers for prediction of Alzheimer’s disease, Neurosci. Lett. 352 (2003) 67–69. Crossref, Medline, Web of ScienceGoogle Scholar
    • 10. J. M. Górriz, F. Segovia, J. Ramírez, A. Lassl and D. Salas-Gonzalez, GMM based SPECT image classification for the diagnosis of Alzheimer’s disease, Appl. Soft Comput. 11 (2011) 2313–2325. Crossref, Web of ScienceGoogle Scholar
    • 11. S. H. Hojjati, A. Ebrahimzadeh, A. Khazaee and A. Babajani-Feremi, Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM, J. Neurosci. Methods 282 (2017) 69–80. Crossref, Medline, Web of ScienceGoogle Scholar
    • 12. S. R. Meikle, F. J. Beekman and S. E. Rose, Complementary molecular imaging technologies: High resolution SPECT, PET and MRI, Drug Discov. Today Technol. 3 (2006) 187–194. Crossref, MedlineGoogle Scholar
    • 13. D. Pan et al., Early detection of Alzheimer’s disease using magnetic resonance imaging: A novel approach combining convolutional neural networks and ensemble learning, Front. Neurosci. 14 (2020) 259. Crossref, Medline, Web of ScienceGoogle Scholar
    • 14. C. Babiloni et al., Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer’s and Parkinson’s diseases, Clin. Neurophysiol. 129 (2018) 766–782. Crossref, Medline, Web of ScienceGoogle Scholar
    • 15. Z. S. Nasreddine et al., The Montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment, J. Am. Geriatr. Soc. 53 (2005) 695–699. Crossref, Medline, Web of ScienceGoogle Scholar
    • 16. I. Arevalo-Rodriguez et al., Mini-mental state examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI), Cochrane Database Syst. Rev. 7 (2021) CD010783. Medline, Web of ScienceGoogle Scholar
    • 17. N. N. Kulkarni and V. K. Bairagi, Extracting salient features for EEG-based diagnosis of Alzheimer’s disease using support vector machine classifier, IETE J. Res. 63 (2017) 11–22. Crossref, Web of ScienceGoogle Scholar
    • 18. M. S. Mendiondo, J. W. Ashford, R. J. Kryscio and F. A. Schmitt, Modeling mini mental state examination changes in Alzheimer’s disease, Stat. Med. 19 (2000) 1607–1616. Crossref, Medline, Web of ScienceGoogle Scholar
    • 19. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease, Neurosci. Lett. 444 (2008) 190–194. Crossref, Medline, Web of ScienceGoogle Scholar
    • 20. C. Ieracitano, N. Mammone, A. Hussain and F. C. Morabito, A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia, Neural Netw. 123 (2020) 176–190. Crossref, Medline, Web of ScienceGoogle Scholar
    • 21. L. R. Trambaiolli, N. Spolaôr, A. C. Lorena, R. Anghinah and J. R. Sato, Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease, Clin. Neurophysiol. 128 (2017) 2058–2067. Crossref, Medline, Web of ScienceGoogle Scholar
    • 22. S. J. Ruiz-Gómez et al., Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment, Entropy 20 (2018) 35. Crossref, Medline, Web of ScienceGoogle Scholar
    • 23. J. C. McBride et al., Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease, Comput. Methods Programs Biomed. 114 (2014) 153–163. Crossref, Medline, Web of ScienceGoogle Scholar
    • 24. K. D. Tzimourta et al., EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions, Brain Sci. 9 (2019) 81. Crossref, Medline, Web of ScienceGoogle Scholar
    • 25. B. Oltu, M. F. Akşahin and S. Kibaroğlu, Investigation of EEG signal for diagnosis of mild cognitive impairment and Alzheimer’s disease, in 2019 Medical Technologies Congress (TIPTEKNO) (IEEE, 2019), pp. 1–4, https://doi.org/10.1109/TIPTEKNO.2019.8895256. CrossrefGoogle Scholar
    • 26. P. M. Rodrigues et al., Lacsogram: A new EEG tool to diagnose Alzheimer’s disease, IEEE J. Biomed. Health Inform. 25 (2021) 3384–3395. Crossref, Medline, Web of ScienceGoogle Scholar
    • 27. J. P. Amezquita-Sanchez, A. Adeli and H. Adeli, A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG), Behav. Brain Res. 305 (2016) 174–180. Crossref, Medline, Web of ScienceGoogle Scholar
    • 28. C. Gómez et al., Synchrony analysis of spontaneous MEG activity in Alzheimer’s disease patients, in 2012 Annual Int. Conf. IEEE Engineering in Medicine and Biology Society (IEEE, 2012), pp. 6188–6191, https://doi.org/10.1109/EMBC.2012.6347407. Google Scholar
    • 29. S. Pusil, S. I. Dimitriadis, M. E. López, E. Pereda and F. Maestú, Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer’s disease, NeuroImage Clin. 24 (2019) 101972. Crossref, Medline, Web of ScienceGoogle Scholar
    • 30. D. A. Blanco-Mora, Y. Almeida, C. Vieira and S. B. Badia, A study on EEG power and connectivity in a virtual reality bimanual rehabilitation training system, in 2019 IEEE Int. Conf. Systems, Man and Cybernetics (SMC) (IEEE, 2019), pp. 2818–2822, https://doi.org/10.1109/SMC.2019.8914190. CrossrefGoogle Scholar
    • 31. D. Kim and K. Kim, Detection of early stage Alzheimer’s disease using EEG relative power with deep neural network, in 2018 40th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2018), pp. 352–355, https://doi.org/10.1109/EMBC.2018.8512231. Google Scholar
    • 32. A. H. H. Al-Nuaimi, E. Jammeh, L. Sun and E. Ifeachor, Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease, Complexity 2018 (2018) 1–12. Crossref, Web of ScienceGoogle Scholar
    • 33. J. P. Amezquita-Sanchez, N. Mammone, F. C. Morabito and H. Adeli, A new dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms, Clin. Neurol. Neurosurg. 201 (2021) 106446. Crossref, Medline, Web of ScienceGoogle Scholar
    • 34. J. delEtoile and H. Adeli, Graph theory and brain connectivity in Alzheimer’s disease, Neuroscientist 23 (2017) 616–626. Crossref, Medline, Web of ScienceGoogle Scholar
    • 35. N. Mammone, C. Ieracitano, H. Adeli, A. Bramanti and F. C. Morabito, Permutation Jaccard distance-based hierarchical clustering to estimate EEG network density modifications in MCI subjects, IEEE Trans. Neural Netw. Learning Syst. 29 (2018) 5122–5135. Crossref, Web of ScienceGoogle Scholar
    • 36. C. Porcaro, F. Vecchio, F. Miraglia, G. Zito and P. M. Rossini, Dynamics of the ‘cognitive’ brain wave P3b at rest for Alzheimer dementia prediction in mild cognitive impairment, Int. J. Neural Syst. 32 (2022) 2250022. Link, Web of ScienceGoogle Scholar
    • 37. M. Cecchi et al., A clinical trial to validate event-related potential markers of Alzheimer’s disease in outpatient settings, Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 1 (2018) 387–394. Google Scholar
    • 38. R. Cassani, M. Estarellas, R. San-Martin, F. J. Fraga and T. H. Falk, Systematic review on resting-state EEG for alzheimer’s disease diagnosis and progression assessment, Dis. Markers 2018 (2018) e5174815, https://www.hindawi.com/journals/dm/2018/5174815/. Crossref, Medline, Web of ScienceGoogle Scholar
    • 39. E. Perez-Valero, M. A. Lopez-Gordo, C. Morillas, F. Pelayo and M. A. Vaquero-Blasco, A review of automated techniques for assisting the early detection of alzheimer’s disease with a focus on EEG, J. Alzheimer’s Dis. 80(4) (2021) 1363–1376, Crossref, Medline, Web of ScienceGoogle Scholar
    • 40. A. Ortiz, J. Munilla, J. M. Górriz and J. Ramírez, Ensembles of deep learning architectures for the early diagnosis of the alzheimer’s disease, Int. J. Neur. Syst. 26 (2016) 1650025. Link, Web of ScienceGoogle Scholar
    • 41. K. D. Tzimourta et al., Machine learning algorithms and statistical approaches for alzheimer’s disease analysis based on resting-state EEG recordings: A systematic review, Int. J. Neural. Syst. 31 (2021) 2130002. Link, Web of ScienceGoogle Scholar
    • 42. L. Khedher et al., Independent component analysis-support vector machine-based computer-aided diagnosis system for alzheimer’s with visual support, Int. J. Neur. Syst. 27 (2017) 1650050. Link, Web of ScienceGoogle Scholar
    • 43. O. K. Cura, A. Akan, G. C. Yilmaz and H. S. Ture, Detection of alzheimer’s dementia by using signal decomposition and machine learning methods, Int. J. Neural Syst. 32 (2022) 2250042. Link, Web of ScienceGoogle Scholar
    • 44. G. Mirzaei, A. Adeli and H. Adeli, Imaging and machine learning techniques for diagnosis of Alzheimer’s disease, Rev. Neurosci. 27 (2016) 857–870. Crossref, Medline, Web of ScienceGoogle Scholar
    • 45. J. P. Amezquita-Sanchez, N. Mammone, F. C. Morabito, S. Marino and H. Adeli, A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals, J. Neurosci. Methods 322 (2019) 88–95. Crossref, Medline, Web of ScienceGoogle Scholar
    • 46. G. Mirzaei and H. Adeli, Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia, Biomed. Signal Process. Control 72 (2022) 103293. Crossref, Web of ScienceGoogle Scholar
    • 47. G. Fiscon et al., Combining EEG signal processing with supervised methods for Alzheimer’s patients classification, BMC Med. Inform. Decis. Mak. 18 (2018) 35. Crossref, Medline, Web of ScienceGoogle Scholar
    • 48. F. Hatz et al., Microstate connectivity alterations in patients with early Alzheimer’s disease, Alz. Res. Therapy 7 (2015) 78. Crossref, Medline, Web of ScienceGoogle Scholar
    • 49. A. Sohrabpour et al., Effect of EEG electrode number on epileptic source localization in pediatric patients, Clin. Neurophysiol. 126 (2015) 472–480. Crossref, Medline, Web of ScienceGoogle Scholar
    • 50. Z. Akalin Acar and S. Makeig, Effects of forward model errors on EEG source localization, Brain Topogr. 26 (2013) 378–396. Crossref, Medline, Web of ScienceGoogle Scholar
    • 51. A. Lendínez González, C. Carnero Pardo, C. Iribar Ibabe, M. V. Zunzunegui Pastor and R. González Maldonado, Análisis de los ítems que componen la ‘Batería Abreviada Granada de Evaluación Neuropsicológica’, Geriátrika (Madr.) (2000) 141–154. Google Scholar
    • 52. C. Carnero-Pardo, S. Lopez-Alcalde, R. F. Allegri and M. J. Russo, A systematic review and meta-analysis of the diagnostic accuracy of the Phototest for cognitive impairment and dementia, Dement. Neuropsychol. 8 (2014) 141–147. Crossref, MedlineGoogle Scholar
    • 53. M. J. Russo et al., Diagnostic accuracy of the Phototest for cognitive impairment and dementia in Argentina, Clin. Neuropsychol. 28 (2014) 826–840. Crossref, Medline, Web of ScienceGoogle Scholar
    • 54. E. Perez-Valero et al., An automated approach for the detection of alzheimer’s disease from resting state electroencephalography, Front. Neuroinform. 16 (2022) 924547. Crossref, Medline, Web of ScienceGoogle Scholar
    • 55. E. Perez-Valero, M. Á. Lopez-Gordo, C. M. Gutiérrez, I. Carrera-Muñoz and R. M. Vílchez-Carrillo, A self-driven approach for multi-class discrimination in Alzheimer’s disease based on wearable EEG, Comput. Methods Progr. Biomed. 220 (2022) 106841. Crossref, Medline, Web of ScienceGoogle Scholar
    • 56. M. Jas, D. A. Engemann, Y. Bekhti, F. Raimondo and A. Gramfort, Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage 159 (2017) 417–429. Crossref, Medline, Web of ScienceGoogle Scholar
    • 57. J. Minguillon, M. A. Lopez-Gordo and F. Pelayo, Trends in EEG-BCI for daily-life: Requirements for artifact removal, Biomed. Signal Process. Contr. 31 (2017) 407–418. Crossref, Web of ScienceGoogle Scholar
    • 58. A. Horvath et al., EEG and ERP biomarkers of Alzheimer’s disease: A critical review, Front. Biosci. 23 (2018) 183–220. Crossref, Medline, Web of ScienceGoogle Scholar
    • 59. C. Coronel et al., Quantitative EEG markers of entropy and auto mutual information in relation to MMSE scores of probable alzheimer’s disease patients, Entropy 19 (2017) 130. Crossref, Web of ScienceGoogle Scholar
    • 60. Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B (Methodol.) 57 (1995) 289–300. Google Scholar
    • 61. F. J. Fraga et al., Towards an EEG-based biomarker for Alzheimer’s disease: Improving amplitude modulation analysis features, in 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing (2013), pp. 1207–1211, https://doi.org/10.1109/ICASSP.2013.6637842. Google Scholar
    • 62. R. Cassani et al., Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices, Biomed. Signal Process. Contr. 33 (2017) 261–271. Crossref, Web of ScienceGoogle Scholar
    • 63. N. Benz et al., Slowing of EEG background activity in parkinson’s and alzheimer’s disease with early cognitive dysfunction, Front. Aging Neurosci. 6 (2014), Article 314, pp. 1–6. Crossref, Medline, Web of ScienceGoogle Scholar
    • 64. J. A. van Deursen, E. F. P. M. Vuurman, F. R. J. Verhey, V. H. J. M. van Kranen-Mastenbroek and W. J. Riedel, Increased EEG gamma band activity in Alzheimer’s disease and mild cognitive impairment, J. Neural Transm. 115 (2008) 1301–1311. Crossref, Medline, Web of ScienceGoogle Scholar
    • 65. J. A. van Deursen, E. F. P. M. Vuurman, V. H. J. M. van Kranen-Mastenbroek, F. R. J. Verhey and W. J. Riedel, 40-Hz steady state response in Alzheimer’s disease and mild cognitive impairment, Neurobiol. Aging 32 (2011) 24–30. Crossref, Medline, Web of ScienceGoogle Scholar
    • 66. P. Ghorbanian et al., Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform, Med. Biol. Eng. Comput. 53 (2015) 843–855. Crossref, Medline, Web of ScienceGoogle Scholar
    • 67. S. Simons and D. Abásolo, Distance-based Lempel–Ziv complexity for the analysis of electroencephalograms in patients with alzheimer’s disease, Entropy 19 (2017) 129. Crossref, Web of ScienceGoogle Scholar
    • 68. H. Aghajani, E. Zahedi, M. Jalili, A. Keikhosravi and B. V. Vahdat, Diagnosis of early alzheimer’s disease based on EEG source localization and a standardized realistic head model, IEEE J. Biomed. Health Inform. 17 (2013) 1039–1045. Crossref, Medline, Web of ScienceGoogle Scholar
    • 69. G. Fiscon et al., Alzheimer’s disease patients classification through EEG signals processing, in 2014 IEEE Symp. Computational Intelligence and Data Mining (CIDM) (IEEE, 2014), pp. 105–112, https://doi.org/10.1109/CIDM.2014.7008655. Google Scholar
    • 70. F. C. Morabito et al., Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer’s disease patients from scalp EEG recordings, in 2016 IEEE 2nd Int. Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) (IEEE, 2016), pp. 1–6, https://doi.org/10.1109/RTSI.2016.7740576. Google Scholar
    • 71. L. R. Trambaiolli et al., Improving Alzheimer’s disease diagnosis with machine learning techniques, Clin. EEG Neurosci. 42 (2011) 160–165. Crossref, Medline, Web of ScienceGoogle Scholar
    • 72. R. Wang et al., Power spectral density and coherence analysis of Alzheimer’s EEG, Cogn. Neurodyn. 9 (2015) 291–304. Crossref, Medline, Web of ScienceGoogle Scholar
    • 73. A. Mazaheri et al., EEG oscillations during word processing predict MCI conversion to Alzheimer’s disease, NeuroImage Clin. 17 (2018) 188–197. Crossref, Medline, Web of ScienceGoogle Scholar
    • 74. S. Khatun, B. I. Morshed and G. M. Bidelman, A single-channel EEG-based approach to detect mild cognitive impairment via speech-evoked brain responses, IEEE Trans. Neural Syst. Rehabil. Eng. 27 (2019) 1063–1070. Crossref, Medline, Web of ScienceGoogle Scholar
    • 75. P. Durongbhan et al., A dementia classification framework using frequency and time-frequency features based on EEG signals, IEEE Trans. Neural Syst. Rehabil. Eng. 27 (2019) 826–835. Crossref, Medline, Web of ScienceGoogle Scholar
    • 76. S. Yang, J. M. S. Bornot, K. Wong-Lin and G. Prasad, M/EEG-based bio-markers to predict the MCI and alzheimer’s disease: A review from the ML perspective, IEEE Trans. Biomed. Eng. 66 (2019) 2924–2935. Crossref, Medline, Web of ScienceGoogle Scholar
    • 77. M. Ahmadlou, H. Adeli and A. Adeli, New diagnostic EEG markers of the Alzheimer’s disease using visibility graph, J. Neural Transm. 117 (2010) 1099–1109. Crossref, Medline, Web of ScienceGoogle Scholar
    Remember to check out the Most Cited Articles!

    Check out our titles in neural networks today!