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EEG-Based Classification Between Individuals with Mild Cognitive Impairment and Healthy Controls Using Conformal Kernel-Based Fuzzy Support Vector Machine

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Abstract

Dementia due to Alzheimer’s disease has become a global health burden. Early detection of one of its potential prodromal state, mild cognitive impairment (MCI), appears to be critical for early interventions. The present study, therefore, addressed the challenging issue of classifying individuals with MCI vs. healthy controls (HC) based on resting-state electroencephalography (EEG). Specifically, we design a feature extraction and feature fusion framework to extract discriminative features. In the first step, we extracted the sample entropy (SampEn) and Katz’s fractal dimension (KFD) features from their corresponding best electrodes selected by a wrapper-based algorithm. Next, the two best feature sets were fused by a principal component analysis (PCA) method. The eigen-complexity pattern (ECP) feature extracted by this framework was subsequently fed into the proposed MCI detector, the conformal kernel-based fuzzy support vector machine (CKF-SVM). The CKF-SVM not only adopts a fuzzified penalty strategy to avoid the overfitting often observed in conventional SVM due to outliers, but also applies a conformally transformed kernel to further increase the class separability. The results carried out on 51 participants (24 MCI, 27 HC) and leave-one-participant-out cross-validation (LOPO-CV) show that the ECP feature combined with a simple linear discriminant analysis classifier achieved an accuracy of 84.31%, higher than the one by either of the two complexity measures (SampEn and KFD), as well as the ones by spectral powers of different scalp regions. The results also show that CKF-SVM outperformed SVM and other classifiers commonly used in the MCI-HC classification studies. A high LOPO-CV accuracy 90.19% (sensitivity = 87.50%, specificity = 92.60%) was achieved by using the ECP feature and the CKF-SVM classifier. These results suggested that the proposed approach has a potential for develo** a sensitive EEG-based computer-aided diagnosis (CAD) system that may, in the future, provide an objective measure to assist physicians’ diagnose of MCI and even a neurofeedback brain–computer interface (BCI) system for monitoring the intervention response of individuals with MCI.

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References

  1. Petersen, R.C., Doody, R., Kurz, A., Mohs, R.C., Morris, J.C., Rabins, P.V., et al.: Current concepts in mild cognitive impairment. Arch. Neurol. 58(12), 1985–1992 (2001)

    Article  Google Scholar 

  2. Vinters, H.V.: Emerging concepts in Alzheimer’s disease. Annu. Rev. Pathol. 10, 291–319 (2015)

    Article  Google Scholar 

  3. Petersen, R.C., Lopez, O., Armstrong, M.J., Getchius, T.S.D., Ganguli, M., Gloss, D., et al.: Practice guideline update summary: Mild cognitive impairment: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology 90(3), 126–135 (2018)

    Article  Google Scholar 

  4. Ward, A., Tardiff, S., Dye, C., Arrighi, H.M.: Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: a systematic review of the literature. Dement. Geriatr. Cogn. Disord. Extra 3(1), 320–332 (2013)

    Article  Google Scholar 

  5. Rodakowski, J., Saghafi, E., Butters, M.A., Skidmore, E.R.: Non-pharmacological interventions for adults with mild cognitive impairment and early stage dementia: An updated sco** review. Mol. Aspects Med. 43, 38–53 (2015)

    Article  Google Scholar 

  6. Sherman, D.S., Mauser, J., Nuno, M., Sherzai, D.: The efficacy of cognitive intervention in mild cognitive impairment (MCI): a meta-analysis of outcomes on neuropsychological measures. Neuropsychol. Rev. 27(4), 440–484 (2017)

    Article  Google Scholar 

  7. Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., et al.: Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396(10248), 413–446 (2020)

    Article  Google Scholar 

  8. Grunwald, M., Busse, F., Hensel, A., Riedel-Heller, S., Kruggel, F., Arendt, T., et al.: Theta-power differences in patients with mild cognitive impairment under rest condition and during haptic tasks. Alzheimer Dis. Assoc. Disord. 16(1), 40–48 (2002)

    Article  Google Scholar 

  9. Moretti, D.V., Miniussi, C., Frisoni, G., Zanetti, O., Binetti, G., Geroldi, C., et al.: Vascular damage and EEG markers in subjects with mild cognitive impairment. Clin. Neurophysiol. 118(8), 1866–1876 (2007)

    Article  Google Scholar 

  10. Rossini, P.M., Buscema, M., Capriotti, M., Grossi, E., Rodriguez, G., Percio, C.D., et al.: Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? Clin. Neurophysiol. 119(7), 1534–1545 (2008)

    Article  Google Scholar 

  11. Prichep, L.S., John, E.R., Ferris, S.H., Rausch, L., Fang, Z., Cancro, R., et al.: Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiol. Aging 27(3), 471–481 (2006)

    Article  Google Scholar 

  12. Yener, G.G., Emek-Savaş, D.D., Lizio, R., Çavuşoğlu, B., Carducci, F., Ada, E., et al.: Frontal delta event-related oscillations relate to frontal volume in mild cognitive impairment and healthy controls. Int. J. Psychophysiol. 103, 110–117 (2016)

    Article  Google Scholar 

  13. Kashefpoor, M., Rabbani, H., Barekatain, M.: Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features. J. Med. Signals Sens. 6(1), 25–32 (2016)

    Article  Google Scholar 

  14. Kashefpoor, M., Rabbani, H., Barekatain, M.: Supervised dictionary learning of EEG signals for mild cognitive impairment diagnosis. Biomed. Signal Process. Control 53, 101559 (2019)

    Article  Google Scholar 

  15. Ruiz-Gómez, S.J., Gómez, C., Poza, J., Gutiérrez-Tobal, G.C., Tola-Arribas, M.A., Cano, M., et al.: Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy 20(1), 35 (2018)

    Article  Google Scholar 

  16. Musaeus, C.S., Engedal, K., Høgh, P., Jelic, V., Mørup, M., Naik, M., et al.: EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease. J. Alzheimer’s Dis. 64(4), 1359–1371 (2018)

    Article  Google Scholar 

  17. Farina, F.R., Emek-Savas, D.D., Rueda-Delgado, L., Boyle, R., Kiiski, H., Yener, G., et al.: A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment. Neuroimage 215, 116795 (2020)

    Article  Google Scholar 

  18. Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A., Morabito, F.C.: A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing 323(5), 96–107 (2019)

    Article  Google Scholar 

  19. Candelas, A.M., Gomez, C., Poza, J., Pinto, N., Hornero, R.: EEG characterization of the Alzheimer’s disease continuum by means of multiscale entropies. Entropy 21(6), 544 (2019)

    Article  Google Scholar 

  20. Cao, Y., Cai, L., Wang, J., Wang, R., Yu, H., Cao, Y., et al.: Characterization of complexity in the electroencephalograph activity of Alzheimer’s disease based on fuzzy entropy. Chaos 25, 8 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Abásolo, D., Hornero, R., Gómez, C., García, M., López, M.: Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Med Eng Phys 28(4), 315–322 (2006)

    Article  Google Scholar 

  22. Abásolo, D., Escudero, J., Hornero, R., Gómez, C., Espino, P.: Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput 46(10), 1019–1028 (2008)

    Article  Google Scholar 

  23. Sharma, N., Kolekar, M.H., Jha, K., Kumar, Y.: EEG and cognitive biomarkers based mild cognitive impairment diagnosis. Irbm 40(2), 113–121 (2019)

    Article  Google Scholar 

  24. Smits, F.M., Porcaro, C., Cottone, C., Cancelli, A., Rossini, P.M., Tecchio, F.: Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease. PLoS ONE 11, 2 (2016)

    Article  Google Scholar 

  25. Esteller, R., Vachtsevanos, G., Echauz, J., Litt, B.: A comparison of waveform fractal dimension. IEEE Trans. Circ. Syst. I Regul. Pap 48, 2 (2001)

    Google Scholar 

  26. Katz, M.J.: Fractals and the analysis of waveforms. Comput. Biol. Med. 18(3), 145–156 (1988)

    Article  Google Scholar 

  27. Yeh, S.C., Hou, C.L., Peng, W.H., Wei, Z.Z., Huang, S., Kung, E.Y.C., et al.: A multiplayer online car racing virtual-reality game based on internet of brains. J. Syst. Archit 89, 30–40 (2018)

    Article  Google Scholar 

  28. Liu, Y.H., Huang, S.A., Huang, Y.D.: Motor imagery EEG Classification for patients with amyotrophic lateral sclerosis using fractal dimension and Fisher’s criterion-based channel selection. Sensors 17, 1557 (2017)

    Article  Google Scholar 

  29. Saha, S., Baumert, M.: Intra- and inter-subject variability in EEG-based sensorimotor brain computer interface: a review. Front. Comput. Neurosci. 13, 87 (2020)

    Article  Google Scholar 

  30. Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Trans. Neural Netw. 13, 464–471 (2002)

    Article  Google Scholar 

  31. Huang, H.P., Liu, Y.H.: Fuzzy support vector machines for pattern recognition and data mining. Int. J. Fuzzy Syst. 4(3), 826–835 (2002)

    MathSciNet  Google Scholar 

  32. Liu, Y.H., Chen, Y.T.: Face recognition using total margin-based adaptive fuzzy support vector machines. IEEE Trans. Neural Netw. 18(1), 178–192 (2007)

    Article  Google Scholar 

  33. Hsu, W.C., Lin, L.F., Chou, C.W., Hsiao, Y.T., Liu, Y.H.: EEG classification of imaginary lower limb step** movements based on fuzzy support vector machine with kernel-induced membership function. Int. J. Fuzzy Syst. 19(2), 566–579 (2017)

    Article  MathSciNet  Google Scholar 

  34. Wu, S., Amari, S.I.: Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers. Neural Process Lett. 15, 59–67 (2002)

    Article  MATH  Google Scholar 

  35. Liu, Y.H., Wu, C.T., Cheng, W.T., Hsiao, Y.T., Chen, P.M., Teng, J.T.: Emotion recognition from single-trial EEG based on kernel Fisher’s emotion pattern and imbalanced quasiconformal kernel support vector machine. Sensors 14, 13361–13388 (2014)

    Article  Google Scholar 

  36. Wu, C.T., Dillon, D.G., Hsu, H.C., Huang, S., Barrick, E., Liu, Y.H.: Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Appl. Sci. 8, 8 (2018)

    Google Scholar 

  37. Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., et al.: The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers. Dement. 7(3), 270–279 (2011)

    Article  Google Scholar 

  38. McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R., Kawas, C.H., et al.: 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. Alzheimers Dement. 7(3), 263–269 (2011)

    Article  Google Scholar 

  39. Tsai, C.F., Lee, W.J., Wang, S.J., Shia, B.C., Nasreddine, Z., Fuh, J.L.: Psychometrics of the Montreal Cognitive Assessment (MoCA) and its subscales: validation of the Taiwanese version of the MoCA and an item response theory analysis. Int. Psychogeriatr. 24(4), 651–658 (2012)

    Article  Google Scholar 

  40. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)

    Article  Google Scholar 

  41. Mognon, Jovicich, J., Bruzzone, L., Buiatti, M.: ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology 48, 229–240 (2011)

    Article  Google Scholar 

  42. Richman, J., Moorman, J.: Physiological time-series analysis using approximate entropy and sample entropy, American Journal of Physiology-Heart and Circulatory. Am. J. Physiol. Heart Circ. Physiol. 278(6), 2039–2049 (2000)

    Article  Google Scholar 

  43. Jiang, G.J., Fan, S.Z., Abbod, M.F., Huang, H.H., Lan, J.Y., Tsai, F.F., et al.: Sample entropy analysis of EEG signals via artificial neural networks to model patients’ consciousness level based on anesthesiologists experience. BioMed Res. Int. 2015(3), 1–8 (2015)

    Google Scholar 

  44. Liu, Y.H., Huang, S., Huang, H.C., Peng, W.H.: Novel motor imagery-based brain switch for patients with amyotrophic lateral sclerosis: a case study using two-channel electroencephalography. IEEE Consum. Electron. Mag. 8(2), 72–77 (2019)

    Article  Google Scholar 

  45. Guyon, Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  46. Liao, S.C., Wu, C.T., Huang, H.C., Cheng, W.T., Liu, Y.H.: Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors 17, 6 (2017)

    Article  Google Scholar 

  47. Narsky, F.C.: Porter, Statistical Analysis Techniques in Particle Physics: Fits, Density Estimation and Supervised Learning. Wiley, Hoboken (2014)

    Google Scholar 

  48. Lu, W., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Netw. 14(1), 117–126 (2003)

    Article  Google Scholar 

  49. Liu, Y.H., Liu, Y.C., Chen, Y.J.: Fast support vector data descriptions for novelty detection. IEEE Trans. Neural Netw. 21(8), 1296–1313 (2010)

    Article  Google Scholar 

  50. Grassberger, P., Procaccia, I.: Measuring the strangeness of attractors. Physica D 9, 189–208 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  51. Grunwald, M., Busse, F., Hensel, A., et al.: Correlation between cortical θ activity and hippocampal volumes in health, mild cognitive impairment, and mild dementia. J. Clin. Neurophysiol 18, 178–184 (2001)

    Article  Google Scholar 

  52. Kasper, S., Bancher, C., Eckert, A., Forstl, H., Frolich, L., Hort, J., et al.: Management of mild cognitive impairment (MCI): The need for national and international guidelines. World J. Biol. Psychiatry 21, 579–594 (2020)

    Article  Google Scholar 

  53. Bishop, N.A., Lu, T., Yankner, B.A.: Neural mechanisms of aging and cognitive decline. Nature 464(7288), 529–535 (2010)

    Article  Google Scholar 

  54. Chodosh, A., Petitti, D.B., Elliott, M., Hays, R.D., Crooks, V.C., Reuben, D.B., et al.: Physician recognition of cognitive impairment: Evaluating the need for improvement. J. Am. Geriatr. Soc. 52, 1051–1059 (2004)

    Article  Google Scholar 

  55. Chan, E., Khan, S., Oliver, R., Gill, S.K., Werring, D.J., Cipolotti, L.: Underestimation of cognitive impairments by the Montreal Cognitive Assessment (MoCA) in an acute stroke unit population. J. Neurol. Sci. 343(1), 176–179 (2014)

    Article  Google Scholar 

  56. Lin, S., Connor, E. O., Rossom, R. C., Perdue, L. A., Burda, B. U., Thompson, M., et al.: Screening for cognitive impairment in older adults: An evidence update for the U.S. Preventive Services Task Force,” Rockville (MD): Agency for Healthcare Research and Quality (US). Report No.: 14-05198-EF-1 (2013).

  57. Elman, A., Jak, A.J., Panizzon, M.S., Tu, X.M., Chen, T., Reynolds, C.A., et al.: Underdiagnosis of mild cognitive impairment: A consequence of ignoring practice effect. Alzheimers Dement. 10, 372–381 (2018)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the funding from the Ministry of Science and Technology (MOST) of Taiwan, under Grant No. MOST 110-2221-E-011-089.

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  1. Yu-Tsung Hsiao, Chien-Te Wu and Chia-Fen Tsai have contributed equally to this paper.

Authors and Affiliations

  1. Graduate Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei, Taiwan

    Yu-Tsung Hsiao

  2. International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, 113-0033, Japan

    Chien-Te Wu

  3. Division of Geriatric Psychiatry, Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan

    Chia-Fen Tsai

  4. Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Chia-Fen Tsai

  5. Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Yi-Hung Liu

  6. Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei, Taiwan

    Thanh-Tung Trinh

  7. Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan

    Chun-Ying Lee

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  1. Yu-Tsung Hsiao
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Hsiao, YT., Wu, CT., Tsai, CF. et al. EEG-Based Classification Between Individuals with Mild Cognitive Impairment and Healthy Controls Using Conformal Kernel-Based Fuzzy Support Vector Machine. Int. J. Fuzzy Syst. 23, 2432–2448 (2021). https://doi.org/10.1007/s40815-021-01186-8

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