Regularized linear discriminant analysis of EEG features in dementia patients

Frontiers in Aging Neuroscience

Volumn 8, Issue NOV, 2016, Pages

  Neto, Emanuel ^a,b   Biessmann, Felix ^c   Aurlien, Harald ^a   Nordby, Helge ^b   Eichele, Tom ^a,b,d  

^a HAUKELAND UNIVERSITY HOSPITAL   (Norway)

^b UNIVERSITY OF BERGEN   (Norway)

^c AMAZON   (United States)

^d KG Jebsen Centre for Research on Neuropsychiatric Disorders   (Norway)

Author keywords

Alzheimer's disease; Electroencephalogram; Group classification; LDA; QEEG; Quantitative analysis; Spectral features; Vascular dementia

Indexed keywords

AGED; ALPHA RHYTHM; ALZHEIMER DISEASE; ARTICLE; BOOTSTRAPPING; CLASSIFIER; CONTROLLED STUDY; DELTA RHYTHM; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIFFERENTIAL DIAGNOSIS; DISCRIMINANT ANALYSIS; ELECTROENCEPHALOGRAM; FEMALE; HUMAN; LINEAR DISCRIMINANT ANALYSIS; MAJOR CLINICAL STUDY; MALE; MULTIINFARCT DEMENTIA; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY;

EID: 85006314373     PISSN: None     EISSN: 16634365     Source Type: Journal    
DOI: 10.3389/fnagi.2016.00273     Document Type: Article

References (79)

1. Anghinah, R., Kanda, P. A., Lopes, H. F., Basile, L. F., Machado, S., Ribeiro, P., et al. (2011). Alzheimer's disease qEEG: spectral analysis versus coherence. Which is the best measurement? Arq. Neuropsiquiatr. 69, 871-874. doi: 10.1590/S0004-282X2011000700004
2. Babiloni, C., Binetti, G., Cassetta, E., Cerboneschi, D., Dal Forno, G., Del Percio, C., et al. (2004). Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study. Neuroimage 22, 57-67. doi: 10.1016/j.neuroimage.2003.09.028
3. Babiloni, C., De Pandis, M. F., Vecchio, F., Buffo, P., Sorpresi, F., Frisoni, G. B., et al. (2011a). Cortical sources of resting state electroencephalographic rhythms in Parkinson's disease related dementia and Alzheimer's disease. Clin. Neurophysiol. 122, 2355-2364. doi: 10.1016/j.clinph.2011.03.029
4. Babiloni, C., Infarinato, F., Marzano, N., Iacoboni, M., Dassu, F., Soricelli, A., et al. (2011b). Intra-hemispheric functional coupling of alpha rhythms is related to golfer's performance: a coherence EEG study. Int. J. Psychophysiol. 82, 260-268. doi: 10.1016/j.ijpsycho.2011.09.008
5. Besserve, M., Jerbi, K., Laurent, F., Baillet, S., Martinerie, J., and Garnero, L. (2007). Classification methods for ongoing EEG and MEG signals. Biol. Res. 40, 415-437. doi: 10.4067/S0716-97602007000500005
6. Besthorn, C., Zerfass, R., Geiger-Kabisch, C., Sattel, H., Daniel, S., Schreiter-Gasser, U., et al. (1997). Discrimination of Alzheimer's disease and normal aging by EEG data. Electroencephalogr. Clin. Neurophysiol. 103, 241-248. doi: 10.1016/S0013-4694(97)96562-7
7. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, Inc
8. Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics. New York, NY: Springer-Verlag
9. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., and Muller, K. R. (2008). Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25, 41-56
10. Bonanni, L., Thomas, A., Tiraboschi, P., Perfetti, B., Varanese, S., and Onofrj, M. (2008). EEG comparisons in early Alzheimer's disease, dementia with Lewy bodies and Parkinson's disease with dementia patients with a 2-year follow-up. Brain 131(Pt 3), 690-705. doi: 10.1093/brain/awm322
11. Box, G. E. P., and Cox, D. R. (1964). An analysis of transformations. J. R. Stat. Soc. Ser. B Stat. Methodol. 26, 211-252
12. Buscema, M., Vernieri, F., Massini, G., Scrascia, F., Breda, M., Rossini, P. M., et al. (2015). An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features. Artif. Intell. Med. 64, 59-74. doi: 10.1016/j.artmed.2015.03.003
13. Cichocki, A., Shishkin, S. L., Musha, T., Leonowicz, Z., Asada, T., and Kurachi, T. (2005). EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease. Clin. Neurophysiol. 116, 729-737. doi: 10.1016/j.clinph.2004.09.017
14. Dauwels, J., Vialatte, F., and Cichocki, A. (2010). Diagnosis of Alzheimer's disease from EEG signals: where are we standing? Curr. Alzheimer Res. 7, 487-505. doi: 10.2174/156720510792231720
15. Delorme, A., and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9-21. doi: 10.1016/j.jneumeth.2003.10.009
16. Dierks, T., Frolich, L., Ihl, R., and Maurer, K. (1995). Correlation between cognitive brain function and electrical brain activity in dementia of Alzheimer type. J. Neural Transm. Gen. Sect. 99, 55-62. doi: 10.1007/BF01271469
17. Dierks, T., Perisic, I., Frolich, L., Ihl, R., and Maurer, K. (1991). Topography of the quantitative electroencephalogram in dementia of the Alzheimer type: relation to severity of dementia. Psychiatry Res. 40, 181-194. doi: 10.1016/0925-4927(91)90009-F
18. Dubois, M. F., and Hebert, R. (2001). The incidence of vascular dementia in Canada: a comparison with Europe and East Asia. Neuroepidemiology 20, 179-187. doi: 10.1159/000054785
19. Elgendi, M., Vialatte, F., Cichocki, A., Latchoumane, C., Jeong, J., and Dauwels, J. (2011). Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 6087-6091. doi: 10.1109/IEMBS.2011.6091504
20. Ernst Niedermeyer, F. H. L. D. S. (2010). "Elecroencephalography," in Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 6th Edn, ed. F. H. L. D. S. Ernst Niedermeyer (New York, NY: Wolters Kluwer/Lippincott Williams & Wilkins Health), 375-387
21. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179-188. doi: 10.1111/j.1469-1809.1936.tb02137.x
22. Fraga, F. J., Falk, T. H., Kanda, P. A., and Anghinah, R. (2013). Characterizing Alzheimer's disease severity via resting-awake EEG amplitude modulation analysis. PLoS ONE 8:e72240. doi: 10.1371/journal.pone.0072240
23. Friedman, J. H. (1989). Regularized discriminant-analysis. J. Am. Stat. Assoc. 84, 165-175. doi: 10.1080/01621459.1989.10478752
24. Gallego-Jutgla, E., Elgendi, M., Vialatte, F., Sole-Casals, J., Cichocki, A., Latchoumane, C., et al. (2012). Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012, 4266-4270. doi: 10.1109/EMBC.2012.6346909
25. Gawel, M., Zalewska, E., Szmidt-Salkowska, E., and Kowalski, J. (2009). The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia. J. Neurol. Sci. 283, 127-133. doi: 10.1016/j.jns.2009.02.332
26. Gearing, M., Mirra, S. S., Hedreen, J. C., Sumi, S. M., Hansen, L. A., and Heyman, A. (1995). The Consortium to establish a registry for Alzheimer's disease (CERAD). Part X. Neuropathology confirmation of the clinical diagnosis of Alzheimer's disease. Neurology 45(3 Pt 1), 461-466
27. Goulermas, J. Y., Findlow, A. H., Nester, C. J., Howard, D., and Bowker, P. (2005). Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data. IEEE Trans. Biomed. Eng. 52, 1549-1562. doi: 10.1109/TBME.2005.851519
28. Guger, C., Ramoser, H., and Pfurtscheller, G. (2000). Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Trans. Rehabil. Eng. 8, 447-456. doi: 10.1109/86.895947
29. Gutman, B. A., Hua, X., Rajagopalan, P., Chou, Y. Y., Wang, Y. L., Yanovsky, I., et al. (2013). Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features. Neuroimage 70, 386-401. doi: 10.1016/j.neuroimage.2012.12.052
30. Haufe, S., Meinecke, F., Gorgen, K., Dahne, S., Haynes, J. D., Blankertz, B., et al. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96-110. doi: 10.1016/j.neuroimage.2013.10.067
31. Hillyard, S. A., and Galambos, R. (1970). Eye movement artifact in CNV. Electroencephalogr. Clin. Neurophysiol. 28, 173-182. doi: 10.1016/0013-4694(70)90185-9
32. Huang, C., Wahlund, L., Dierks, T., Julin, P., Winblad, B., and Jelic, V. (2000). Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study. Clin. Neurophysiol. 111, 1961-1967. doi: 10.1016/S1388-2457(00)00454-5
33. Huang, G., Liu, G., Meng, J., Zhang, D., and Zhu, X. (2010). Model based generalization analysis of common spatial pattern in brain computer interfaces. Cogn. Neurodyn. 4, 217-223. doi: 10.1007/s11571-010-9117-x
34. Huang, Y., Erdogmus, D., Mathan, S., and Pavel, M. (2008). Detecting EEG evoked responses for target image search with mixed effect models. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008, 4988-4991. doi: 10.1109/IEMBS.2008.4650334
35. Jelic, V., Blomberg, M., Dierks, T., Basun, H., Shigeta, M., Julin, P., et al. (1998). EEG slowing and cerebrospinal fluid tau levels in patients with cognitive decline. Neuroreport 9, 157-160. doi: 10.1097/00001756-199801050-00032
36. Jeong, J. (2004). EEG dynamics in patients with Alzheimer's disease. Clin. Neurophysiol. 115, 1490-1505. doi: 10.1016/j.clinph.2004.01.001
37. Kanda, P. A., Trambaiolli, L. R., Lorena, A. C., Fraga, F. J., Basile, L. F., Nitrini, R., et al. (2014). Clinician's road map to wavelet EEG as an Alzheimer's disease biomarker. Clin. EEG Neurosci. 45, 104-112. doi: 10.1177/1550059413486272
38. Kandimalla, R. J., Anand, R., Veeramanikandan, R., Wani, W. Y., Prabhakar, S., Grover, V. K., et al. (2014). CSF ubiquitin as a specific biomarker in Alzheimer's disease. Curr. Alzheimer Res. 11, 340-348. doi: 10.2174/1567205011666140331161027
39. Kandimalla, R. J., Prabhakar, S., Binukumar, B. K., Wani, W. Y., Gupta, N., Sharma, D. R., et al. (2011). Apo-Eepsilon4 allele in conjunction with Abeta42 and tau in CSF: biomarker for Alzheimer's disease. Curr. Alzheimer Res. 8, 187-196. doi: 10.2174/156720511795256071
40. Kandimalla, R. J., Prabhakar, S., Wani, W. Y., Kaushal, A., Gupta, N., Sharma, D. R., et al. (2013). CSF p-Tau levels in the prediction of Alzheimer's disease. Biol. Open 2, 1119-1124. doi: 10.1242/bio.20135447
41. Kaplan, A. Y., Fingelkurts, A. A., Fingelkurts, A. A., Borisov, S. V., and Darkhovsky, B. S. (2005). Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges. Signal Process. 85, 2190-2212. doi: 10.1016/j.sigpro.2005.07.010
42. Kwak, Y. T. (2006). Quantitative EEG findings in different stages of Alzheimer's disease. J. Clin. Neurophysiol. 23, 456-461. doi: 10.1097/01.wnp.0000223453.47663.63
43. Ledoit, O., and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88, 365-411. doi: 10.1016/S0047-259x(03)00096-4
44. Lehmann, C., Koenig, T., Jelic, V., Prichep, L., John, R. E., Wahlund, L. O., et al. (2007). Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG). J. Neurosci. Methods 161, 342-350. doi: 10.1016/j.jneumeth.2006.10.023
45. Lehmann, D., and Skrandies, W. (1980). Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr. Clin. Neurophysiol. 48, 609-621. doi: 10.1016/0013-4694(80)90419-8
46. Lopez, M. M., Ramirez, J., Gorriz, J. M., Alvarez, I., Salas-Gonzalez, D., Segovia, F., et al. (2009). SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA. Neurosci. Lett. 464, 233-238. doi: 10.1016/j.neulet.2009.08.061
47. Lotte, F., and Guan, C. (2011). Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58, 355-362. doi: 10.1109/TBME.2010.2082539
48. Lou, W., Xu, J., Sheng, H., and Zhao, S. (2011). Multichannel linear descriptors analysis for event-related EEG of vascular dementia patients during visual detection task. Clin. Neurophysiol. 122, 2151-2156. doi: 10.1016/j.clinph.2011.03.021
49. Maggi, L., Parini, S., Piccini, L., Panfili, G., and Andreoni, G. (2006). A four command BCI system based on the SSVEP protocol. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 1264-1267. doi: 10.1109/IEMBS.2006.260353
50. Massoud, F., Devi, G., Stern, Y., Lawton, A., Goldman, J. E., Liu, Y., et al. (1999). A clinicopathological comparison of community-based and clinic-based cohorts of patients with dementia. Arch. Neurol. 56, 1368-1373. doi: 10.1001/archneur.56.11.1368
51. McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R. Jr., Kawas, C. H., et al. (2011). 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, 263-269. doi: 10.1016/j.jalz.2011.03.005
52. Moretti, D. V., Zanetti, O., Binetti, G., and Frisoni, G. B. (2012). Quantitative EEG markers in mild cognitive impairment: degenerative versus vascular brain impairment. Int. J. Alzheimers Dis. 2012:917537. doi: 10.1155/2012/917537
53. Neto, E., Allen, E. A., Aurlien, H., Nordby, H., and Eichele, T. (2015). Electroencephalogram (EEG) spectral features discriminate between Alzheimer's (AD) and Vascular dementia (VaD). Front. Neurol. 6:25. doi: 10.3389/fneur.2015.00025
54. Nunez, P. L., and Srinivasan, R. (2006). Electric fields of the brain: the neurophysics of EEG, 2nd Edn. Oxford: Oxford University Press
55. Prichep, L. S. (2005). Use of normative databases and statistical methods in demonstrating clinical utility of QEEG: importance and cautions. Clin. EEG Neurosci. 36, 82-87. doi: 10.1177/155005940503600207
56. Ramoser, H., Muller-Gerking, J., and Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8, 441-446. doi: 10.1109/86.895946
57. Reed, B. R., Eberling, J. L., Mungas, D., Weiner, M., Kramer, J. H., and Jagust, W. J. (2004). Effects of white matter lesions and lacunes on cortical function. Arch. Neurol. 61, 1545-1550. doi: 10.1001/archneur.61.10.1545
58. Reilly, J., Rodriguez, A. D., Lamy, M., and Neils-Strunjas, J. (2010). Cognition, language, and clinical pathological features of non-Alzheimer's dementias: an overview. J. Commun. Disord. 43, 438-452. doi: 10.1016/j.jcomdis.2010.04.011
59. Reuderink, B., and Poel, M. (2008). Robustness of the Common Spatial Patterns algorithm in the BCI-pipeline. Enschede: University of Twente
60. Rodriguez, G., Nobili, F., Rocca, G., De Carli, F., Gianelli, M. V., and Rosadini, G. (1998). Quantitative electroencephalography and regional cerebral blood flow: discriminant analysis between Alzheimer's patients and healthy controls. Dement. Geriatr. Cogn. Disord. 9, 274-283. doi: 10.1159/000017072
61. Roman, G. C. (2002). Vascular dementia revisited: diagnosis, pathogenesis, treatment, and prevention. Med. Clin. North Am. 86, 477-499. doi: 10.1016/S0025-7125(02)00008-1
62. Rossini, P. M., Rossi, S., Babiloni, C., and Polich, J. (2007). Clinical neurophysiology of aging brain: from normal aging to neurodegeneration. Prog. Neurobiol. 83, 375-400. doi: 10.1016/j.pneurobio.2007.07.010
63. Shenoy, P., Krauledat, M., Blankertz, B., Rao, R. P., and Muller, K. R. (2006). Towards adaptive classification for BCI. J. Neural. Eng. 3, R13-R23. doi: 10.1088/1741-2560/3/1/R02
64. Shenoy, P., Miller, K. J., Ojemann, J. G., and Rao, R. P. (2008). Generalized features for electrocorticographic BCIs. IEEE Trans. Biomed. Eng. 55, 273-280. doi: 10.1109/TBME.2007.903528
65. Signorino, M., Pucci, E., Belardinelli, N., Nolfe, G., and Angeleri, F. (1995). EEG spectral analysis in vascular and Alzheimer dementia. Electroencephalogr. Clin. Neurophysiol. 94, 313-325. doi: 10.1016/0013-4694(94)00290-2
66. Signorino, M., Pucci, E., Brizioli, E., Cacchio, G., Nolfe, G., and Angeleri, F. (1996). EEG power spectrum typical of vascular dementia in a subgroup of Alzheimer patients. Arch. Gerontol. Geriatr. 23, 139-151. doi: 10.1016/0167-4943(96)00714-5
67. Szelies, B., Mielke, R., Herholz, K., and Heiss, W. D. (1994). Quantitative topographical EEG compared to FDG PET for classification of vascular and degenerative dementia. Electroencephalogr. Clin. Neurophysiol. 91, 131-139. doi: 10.1016/0013-4694(94)90034-5
68. Triba, M. N., Le Moyec, L., Amathieu, R., Goossens, C., Bouchemal, N., Nahon, P., et al. (2015). PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol. Biosyst. 11, 13-19. doi: 10.1039/c4mb00414k
69. Tsuno, N., Shigeta, M., Hyoki, K., Faber, P. L., and Lehmann, D. (2004). Fluctuations of source locations of EEG activity during transition from alertness to sleep in Alzheimer's disease and vascular dementia. Neuropsychobiology 50, 267-272. doi: 10.1159/000079982
70. van Straaten, E. C., de Haan, W., de Waal, H., Scheltens, P., van der Flier, W. M., Barkhof, F., et al. (2012). Disturbed oscillatory brain dynamics in subcortical ischemic vascular dementia. BMC Neurosci. 13:85. doi: 10.1186/1471-2202-13-85
71. Vecchio, F., Babiloni, C., Lizio, R., Fallani Fde, V., Blinowska, K., Verrienti, G., et al. (2013). Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review. Suppl. Clin. Neurophysiol. 62, 223-236. doi: 10.1016/B978-0-7020-5307-8.00015-6
72. Velu, P. D., and de Sa, V. R. (2013). Single-trial classification of gait and point movement preparation from human EEG. Front. Neurosci. 7:84. doi: 10.3389/fnins.2013.00084
73. von Bunau, P., Meinecke, F. C., Kiraly, F. C., and Muller, K. R. (2009). Finding stationary subspaces in multivariate time series. Phys. Rev. Lett. 103, 214101. doi: 10.1103/PhysRevLett.103.214101
74. Walker, M. P., Ayre, G. A., Cummings, J. L., Wesnes, K., McKeith, I. G., O'Brien, J. T., et al. (2000). Quantifying fluctuation in dementia with Lewy bodies, Alzheimer's disease, and vascular dementia. Neurology 54, 1616-1625. doi: 10.1212/WNL.54.8.1616
75. Welch, B. L. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika 29, 350-362. doi: 10.2307/2332010
76. Worth, A. P., and Cronin, M. T. D. (2003). The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. J. Mol. Struct. Theochem. 622, 97-111. doi: 10.1016/S0166-1280(02)00622-X
77. Ye, J., Xiong, T., Li, Q., Janardan, R., Bi, J., Cherkassky, V., et al. (2006). "Efficient model selection for regularized linear discriminant analysis," in Proceedings of the 15th ACM International Conference on Information and Knowledge Management, (New York, NY: ACM Press)
78. Zhdanov, A., Hendler, T., Ungerleider, L., and Intrator, N. (2007). Inferring functional brain states using temporal evolution of regularized classifiers. Comput. Intell. Neurosci. 2007:52609. doi: 10.1155/2007/52609
79. Zhou, X.-H., McClish, D. K., and Obuchowski, N. A. (2011). Statistical Methods in Diagnostic Medicine, 2nd Edn. Hoboken, NJ: Wiley