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International Journal of Automation and Computing

, Volume 16, Issue 6, pp 786–799 | Cite as

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

  • Jiao YinEmail author
  • Jinli Cao
  • Siuly Siuly
  • Hua Wang
Research Article

Abstract

Aiming to differentiate between mild cognitive impairment (MCI) patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state electroencephalogram (EEG) recordings. This framework firstly eliminates noise by employing stationary wavelet transformation (SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional (3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the support vector machine (SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-nearest neighbors (KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delaying cognitive decline and dementia onset.

Keywords

Electroencephalogram (EEG) dementia early detection mild cognitive impairment (MCI) stationary wavelet transformation (SWT) support vector machine (SVM) 

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Notes

Acknowledgements

The first author was supported by a La Trobe University Postgraduate Research Scholarship and a La Trobe University Full Fee Research Scholarship, she was also supported by the Science Foundation of Chongqing University of Arts and Sciences Chongqing, China (No. Z2016RJ15).

References

  1. [1]
    N. Houmani, F. Vialatte, E. Gallego-Jutglà, G. Dreyfus, V. H. Nguyen-Michel, J. Mariani, K. Kinugawa. Diagnosis of Alzheimer’s disease with electroencephalography in a differential framework. PLoS One, vol. 13, no. 3, Article number e0193607, 2018. DOI:  https://doi.org/10.1371/journal.pone.0193607.CrossRefGoogle Scholar
  2. [2]
    M. Kashefpoor, H. Rabbani, M. Barekatain. Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features. Journal of Medical Signals and Sensors, vol. 6, no. 1, pp. 25–32, 2016.CrossRefGoogle Scholar
  3. [3]
    S. Khatun, B. I. Morshed, G. M. Bidelman. Single channel EEG time-frequency features to detect mild cognitive impairment. In Proceedings of IEEE International Symposium on Medical Measurements and Applications, IEEE, Rochester, USA, pp. 437–442, 2017. DOI:  https://doi.org/10.1109/MeMeA.2017.7985916.Google Scholar
  4. [4]
    V. C. Bibina, U. Chakraborty, R. M. Lourde, A. Kumar. Time-frequency methods for diagnosing Alzheimer’s disease using EEG: A technical review. In Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science, ACM, Singapore, pp. 49–54, 2017. DOI:  https://doi.org/10.1145/3121138.3121183.Google Scholar
  5. [5]
    N. K. Al-Qazzaz, S. H. B. Ali, S. A. Ahmad, K. Chellappan, M. S. Islam, J. Escudero. Role of EEG as biomarker in the early detection and classification of dementia. The Scientific World Journal, vol. 2014, Article number 906038, 2014. DOI:  https://doi.org/10.1155/2014/906038.CrossRefGoogle Scholar
  6. [6]
    Z. J. Yao, J. Bi, Y. X. Chen. Applying deep learning to individual and community health monitoring data: A survey. International Journal of Automation and Computing, vol. 15, no. 6, pp. 643–655, 2018. DOI:  https://doi.org/10.1007/s11633-018-1136-9.CrossRefGoogle Scholar
  7. [7]
    F. Vecchio, C. Babiloni, R. Lizio, F. D. V. Fallani, K. Blinowska, G. Verrienti, G. Frisoni, P. M. Rossini. Resting state cortical EEG rhythms in Alzheimer’s disease: Toward EEG markers for clinical applications: A review. Supplements to Clinical Neurophysiology, vol. 62, pp. 223–236, 2013. DOI:  https://doi.org/10.1016/B978-0-7020-5307-8.00015-6.CrossRefGoogle Scholar
  8. [8]
    V. Bajaj, S. Taran, A. Sengur. Emotion classification using flexible analytic wavelet transform for electroencephalogram signals. Health Information Science and Systems, vol. 6, no. 1, Article number 12, 2018. DOI:  https://doi.org/10.1007/s13755-018-0048-y.
  9. [9]
    S. Supriya, S. Siuly, H. Wang, Y. C. Zhang. An efficient framework for the analysis of big brain signals data. In Prceedings of Australasian Database Conference, Springer, Gold Coast, Australia, pp. 199–207, 2018. DOI: {rs 10.1007/978-3-319-92013-9_16 DOI}.Google Scholar
  10. [10]
    S. Supriya, S. Siuly, H. Wang, Y. C. Zhang. EEG sleep stages analysis and classification based on weighed complex network features. IEEE Transactions on Emerging Topics in Computational Intelligence, published online. DOI:  https://doi.org/10.1109/TETCI.2018.2876529.
  11. [11]
    P. A. M. Kanda, E. F. Oliveira, F. J. Fraga. EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer’s. Computer Methods and Programs in Biomedicine, vol. 138, pp. 13–22, 2017. DOI:  https://doi.org/10.1016/j.cmpb.2016.09.023.CrossRefGoogle Scholar
  12. [12]
    H. Garn, C. Coronel, M. Waser, G. Caravias, G. Ransmayr. Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalo-graphic features. Journal of Neural Transmission, vol. 124, no. 5, pp. 569–581, 2017. DOI:  https://doi.org/10.1007/s00702-017-1699-6.CrossRefGoogle Scholar
  13. [13]
    S. Siuly, E. Kabir, H. Wang, Y. C. Zhang. Exploring sampling in the detection of multicategory EEG signals. Computational and Mathematical Methods in Medicine, vol. 2015, Article number 576437, 2015. DOI:  https://doi.org/10.1155/2015/576437.CrossRefGoogle Scholar
  14. [14]
    M. Buscema, E. Grossi, M. Capriotti, C. Babiloni, P. Rossini. The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy. Current Alzheimer Research, vol. 7, no. 2, pp. 173–187, 2010. DOI:  https://doi.org/10.2174/156720510790691137.CrossRefGoogle Scholar
  15. [15]
    E. Barzegaran, B. van Damme, R. Meuli, M. G. Knyazeva. Perception-related EEG is more sensitive to Alzheimer’s disease effects than resting EEG. Neurobiology of Aging, vol. 43, pp. 129–139, 2016. DOI:  https://doi.org/10.1016/j.neurobiolaging.2016.03.032.CrossRefGoogle Scholar
  16. [16]
    P. Ghorbanian, D. M. Devilbiss, A. Verma, A. Bernstein, T. Hess, A. J. Simon, H. Ashrafiuon. Identification of resting and active state EEG features of Alzheimer’s disease using discrete wavelet transform. Annals of Biomedical Engineering, vol. 41, no. 6, pp. 1243–1257, 2013. DOI:  https://doi.org/10.1007/s10439-013-0795-5.CrossRefGoogle Scholar
  17. [17]
    S. S. Poil, W. De Haan, W. M. van der Flier, H. D. Mansvelder, P. Scheltens, K. Linkenkaer-Hansen. Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Frontiers in Aging Neuroscience, vol. 5, Article number 58, 2013. DOI:  https://doi.org/10.3389/fnagi.2013.00058.CrossRefGoogle Scholar
  18. [18]
    F. Liu, X. S. Zhou, J. L. Cao, Z. Wang, H. Wang, Y. C. Zhang. Arrhythmias classification by integrating stacked bidirectional LSTM and two-dimensional CNN. In Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Macau, China, pp. 136–149, 2019. DOI:  https://doi.org/10.1007/978-3-030-16145-3_11.CrossRefGoogle Scholar
  19. [19]
    L. R. Trambaiolli, N. Spolaôr, A. C. Lorena, R. Anghinah, J. R. Sato. Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease. Clinical Neurophysiology, vol. 128, no. 10, pp. 2058–2067, 2017. DOI:  https://doi.org/10.1016/j.clinph.2017.06.251.CrossRefGoogle Scholar
  20. [20]
    R. F. Wang, J. Wang, S. N. Li, H. T. Yu, B. Deng, X. L. Wei. Multiple feature extraction and classification of electroencephalograph signal for Alzheimers’ with spectrum and bispectrum. Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 25, no. 1, Article number 013110, 2015. DOI:  https://doi.org/10.1063/1.4906038.MathSciNetCrossRefGoogle Scholar
  21. [21]
    A. I. Triggiani, V. Bevilacqua, A. Brunetti, R. Lizio, G. Tattoli, F. Cassano, A. Soricelli, R. Ferri, F. Nobili, L. Gesualdo, M. R. Barulli, R. Tortelli, V. Cardinali, A. Giannini, P. Spagnolo, S. Armenise, F. Stocchi, G. Buenza, G. Scianatico, G. Logroscino, G. Lacidogna, F. Orzi, C. Buttinelli, F. Giubilei, C. Del Percio, G. B. Frisoni, C. Babiloni. Classification of healthy subjects and Alzheimer’s disease patients with dementia from cortical sources of resting state EEG rhythms: A study using artificial neural networks. Frontiers in Neuroscience, vol. 10, Article number 604, 2017. DOI:  https://doi.org/10.3389/fnins.2016.00604.CrossRefGoogle Scholar
  22. [22]
    F. Bertè, G. Lamponi, R. S. Calabrò, P. Bramanti. Elman neural network for the early identification of cognitive impairment in Alzheimer’s disease. Functional Neurology, vol. 29, no. 1, pp. 57–65, 2014.Google Scholar
  23. [23]
    S. Afrakhteh, M. R. Mosavi, M. Khishe, A. Ayatollahi. Accurate classification of EEG signals using neural networks trained by hybrid population-physic-based algorithm. International Journal of Automation and Computing, published online. DOI:  https://doi.org/10.1007/s11633-018-1158-3.
  24. [24]
    H. Aghajani, E. Zahedi, M. Jalili, A. Keikhosravi, B. V. Vahdat. Diagnosis of early Alzheimer’s disease based on EEG source localization and a standardized realistic head model. IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 6, pp. 1039–1045, 2013. DOI:  https://doi.org/10.1109/JBHI.2013.2253326.CrossRefGoogle Scholar
  25. [25]
    I. Güler, E. D. Übeyli. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods, vol. 148, no. 2, pp. 113–121, 2005. DOI:  https://doi.org/10.1016/j.jneumeth.2005.04.013.CrossRefGoogle Scholar
  26. [26]
    EEG Signals from Normal and MCI (Mild Cognitive Impairment) Cases, [Online], Available: http://www.biosigdata.com/?download=eeg-signals-from-normal-and-mci-cases, September 15, 2018.
  27. [27]
    J. Vigil, L. Tataryn. Neurotherapies and Alzheimer’s: A protocol-oriented review. NeuroRegulation, vol. 4, no. 2, pp. 79–94, 2017. DOI:  https://doi.org/10.15540/nr.4.2.79.CrossRefGoogle Scholar
  28. [28]
    B. T. Zhang, X. P. Wang, Y. Shen, T. Lei. Dual-modal physiological feature fusion-based sleep recognition using CFS and RF algorithm. International Journal of Automation and Computing, vol. 16, no. 3, pp. 286–296, 2019. DOI:  https://doi.org/10.1007/s11633-019-1171-1.CrossRefGoogle Scholar
  29. [29]
    S. M. Hosni, M. E. Gadallah, S. F. Bahgat, M. S. Abdel-Wahab. Classification of EEG signals using different feature extraction techniques for mental-task BCI. In Proceedings of International Conference on Computer Engineering & Systems, IEEE, Cairo, Egypt, pp. 220–226, 2007. DOI:  https://doi.org/10.1109/ICCES.2007.4447052.Google Scholar
  30. [30]
    F. Liu, X. S. Zhou, Z. Wang, J. L. Cao, H. Wang, Y. C. Zhang. Unobtrusive mattress-based identification of hypertension by integrating classification and association rule mining. Sensors, vol. 19, no. 7, Article number 1489, 2019. DOI:  https://doi.org/10.3390/s19071489.CrossRefGoogle Scholar
  31. [31]
    D. Pandey, X. X. Yin, H. Wang, Y. C. Zhang. Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising. Computer Vision and Image Understanding, vol. 155, pp. 162–172, 2017. DOI:  https://doi.org/10.1016/j.cviu.2016.12.005.CrossRefGoogle Scholar
  32. [32]
    N. K. Al-Qazzaz, S. H. B. M. Ali, S. A. Ahmad, M. S. Islam, J. Escudero. Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors, vol. 15, no. 11, pp. 29015–29035, 2015. DOI:  https://doi.org/10.3390/s151129015.CrossRefGoogle Scholar
  33. [33]
    S. Siuly, V. Bajaj, A. Sengur, Y. C. Zhang. An advanced analysis system for identifying alcoholic brain state through EEG signals. International Journal of Automation and Computing, published online. DOI:  https://doi.org/10.1007/s11633-019-1178-7.
  34. [34]
    C. Lehmann, T. Koenig, V. Jelic, L. Prichep, R. E. John, L. O. Wahlund, Y. Dodge, T. Dierks. Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). Journal of Neuroscience Methods, vol. 161, no. 2, pp. 342–350, 2007. DOI:  https://doi.org/10.1016/j.jneumeth.2006.10.023.CrossRefGoogle Scholar
  35. [35]
    J. C. McBride, X. P. Zhao, N. B. Munro, C. D. Smith, G. A. Jicha, L. Hively, L. S. Broster, F. A. Schmitt, R. J. Kryscio, Y. Jiang. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease. Computer Methods and Programs in Biomedicine, vol. 114, no. 2, pp. 153–163, 2014. DOI:  https://doi.org/10.1016/j.cmpb.2014.01.019.CrossRefGoogle Scholar
  36. [36]
    P. M. Rossini, M. Buscema, M. Capriotti, E. Grossi, G. Rodriguez, C. Del Percio, C. Babiloni. Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? Clinical Neurophysiology, vol. 119, no. 7, pp. 1534–1545, 2008. DOI:  https://doi.org/10.1016/j.clinph.2008.03.026.CrossRefGoogle Scholar
  37. [37]
    G. Fiscon, E. Weitschek, A. Cialini, G. Felici, P. Bertolazzi, S. De Salvo, A. Bramanti, P. Bramanti, M. C. De Cola. Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Medical Informatics and Decision Making, vol. 18, Acticle number 35, 2018. DOI:  https://doi.org/10.1186/s12911-018-0613-y.CrossRefGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information TechnologyLa Trobe UniversityMelboumeAustralia
  2. 2.School of Software EngineeringChongqing University of Arts and SciencesChongqingChina
  3. 3.Institute for Sustainable Industries & Liveable CitiesVictoria UniversityMelboumeAustralia

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