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2018 | OriginalPaper | Buchkapitel

The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia

verfasst von : Luis A. Guerra, Laura C. Lanzarini, Luis E. Sánchez

Erschienen in: Developments and Advances in Defense and Security

Verlag: Springer International Publishing

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Abstract

Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions.

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Literatur
1.
Zurück zum Zitat Griffa, A.: Structural Connectomics in Brain Diseases. Neuroimage. 80, 515–526 (2013)CrossRef Griffa, A.: Structural Connectomics in Brain Diseases. Neuroimage. 80, 515–526 (2013)CrossRef
2.
Zurück zum Zitat Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)CrossRef Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)CrossRef
3.
Zurück zum Zitat Al-Qazzaz, N.K.: Role of EEG as biomarker in the early detection and classification of dementia. Sci. World J. 2014, 16 (2014)CrossRef Al-Qazzaz, N.K.: Role of EEG as biomarker in the early detection and classification of dementia. Sci. World J. 2014, 16 (2014)CrossRef
4.
Zurück zum Zitat Cedazo-Minguez, A., Winblad, B.: Biomarkers for Alzheimer’s disease and other forms of dementia: clinical needs, limitations and future aspects. Exp. Gerontol. 45(1), 5–14 (2010)CrossRef Cedazo-Minguez, A., Winblad, B.: Biomarkers for Alzheimer’s disease and other forms of dementia: clinical needs, limitations and future aspects. Exp. Gerontol. 45(1), 5–14 (2010)CrossRef
5.
Zurück zum Zitat Hampel, H.: Biomarkers for Alzheimer’s Disease: academic, industry and regulatory perspectives. Nat. Rev. Drug Discov. 9(7), 560–574 (2010)CrossRef Hampel, H.: Biomarkers for Alzheimer’s Disease: academic, industry and regulatory perspectives. Nat. Rev. Drug Discov. 9(7), 560–574 (2010)CrossRef
6.
Zurück zum Zitat Vialatte, F.B.: Improving the specificity of EEG for diagnosing Alzheimer’s Disease. Int. J. Alzheimer’s Dis. 2011, 7 (2011) Vialatte, F.B.: Improving the specificity of EEG for diagnosing Alzheimer’s Disease. Int. J. Alzheimer’s Dis. 2011, 7 (2011)
7.
Zurück zum Zitat Hampel, H.: Perspective on future role of biological markers in clinical therapy trials of Alzheimer’s disease: a long-range point of view beyond 2020. Biochem. Pharmacol. 88(4), 426–449 (2014)CrossRef Hampel, H.: Perspective on future role of biological markers in clinical therapy trials of Alzheimer’s disease: a long-range point of view beyond 2020. Biochem. Pharmacol. 88(4), 426–449 (2014)CrossRef
8.
Zurück zum Zitat Borson, S.: Improving dementia care: the role of screening and detection of cognitive impairment. Alzheimer’s Dement. 9(2), 151–159 (2013)CrossRef Borson, S.: Improving dementia care: the role of screening and detection of cognitive impairment. Alzheimer’s Dement. 9(2), 151–159 (2013)CrossRef
9.
Zurück zum Zitat DeKosky, S.T., Marek, K.: Looking backward to move forward: early detection of neurodegenerative disorders. Science 302(5646), 830–834 (2003)CrossRef DeKosky, S.T., Marek, K.: Looking backward to move forward: early detection of neurodegenerative disorders. Science 302(5646), 830–834 (2003)CrossRef
10.
Zurück zum Zitat Román, G.C.: Vascular dementia may be the most common form of dementia in the elderly. J. Neurol. Sci. 203, 7–10 (2002)CrossRef Román, G.C.: Vascular dementia may be the most common form of dementia in the elderly. J. Neurol. Sci. 203, 7–10 (2002)CrossRef
11.
Zurück zum Zitat Thal, D.R., Grinberg, L.T., Attems, J.: Vascular dementia: different forms of vessel disorders contribute to the development of dementia in the elderly brain. Exp. Gerontol. 47(11), 816–824 (2012)CrossRef Thal, D.R., Grinberg, L.T., Attems, J.: Vascular dementia: different forms of vessel disorders contribute to the development of dementia in the elderly brain. Exp. Gerontol. 47(11), 816–824 (2012)CrossRef
12.
Zurück zum Zitat Petersen, R.C.: Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256(3), 183–194 (2004)CrossRef Petersen, R.C.: Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256(3), 183–194 (2004)CrossRef
13.
Zurück zum Zitat Dorval, V., Nelson, P.T., Hébert, S.S.: Circulating MicroRNAs in Alzheimer’s Disease: The Search for Novel Biomarkers. Frontiers in Molecular Neuroscience 6, 24 (2013) Dorval, V., Nelson, P.T., Hébert, S.S.: Circulating MicroRNAs in Alzheimer’s Disease: The Search for Novel Biomarkers. Frontiers in Molecular Neuroscience 6, 24 (2013)
14.
Zurück zum Zitat Poil, S.S.: Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Front. Aging Neurosci. 5, 58 (2013)CrossRef Poil, S.S.: Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Front. Aging Neurosci. 5, 58 (2013)CrossRef
15.
Zurück zum Zitat Mattsson, N.: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302(4), 385–393 (2009)MathSciNetCrossRef Mattsson, N.: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302(4), 385–393 (2009)MathSciNetCrossRef
16.
Zurück zum Zitat Paraskevas, G.: CSF biomarker profile and diagnostic value in vascular dementia. Eur. J. Neurol. 16(2), 205–211 (2009)CrossRef Paraskevas, G.: CSF biomarker profile and diagnostic value in vascular dementia. Eur. J. Neurol. 16(2), 205–211 (2009)CrossRef
17.
Zurück zum Zitat Frankfort, S.V.: Amyloid beta protein and tau in cerebrospinal fluid and plasma as biomarkers for dementia: a review of recent literature. Curr. Clin. Pharmacol. 3(2), 123–131 (2008)CrossRef Frankfort, S.V.: Amyloid beta protein and tau in cerebrospinal fluid and plasma as biomarkers for dementia: a review of recent literature. Curr. Clin. Pharmacol. 3(2), 123–131 (2008)CrossRef
18.
Zurück zum Zitat Folin, M.: Apolipoprotein E as vascular risk factor in neurodegenerative dementia. Int. J. Mol. Med. 14, 609–614 (2004) Folin, M.: Apolipoprotein E as vascular risk factor in neurodegenerative dementia. Int. J. Mol. Med. 14, 609–614 (2004)
19.
Zurück zum Zitat Schneider, A.L., Jordan, K.G.: Regional attenuation without delta (RAWOD): a disqtinctive EEG pattern that can aid in the diagnosis and management of severe acute ischemic stroke. Am. J. Electroneurodiagn. Technol. 45(2), 102–117 (2005) Schneider, A.L., Jordan, K.G.: Regional attenuation without delta (RAWOD): a disqtinctive EEG pattern that can aid in the diagnosis and management of severe acute ischemic stroke. Am. J. Electroneurodiagn. Technol. 45(2), 102–117 (2005)
20.
Zurück zum Zitat Henderson, G.: Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Trans. Biomed. Eng. 53(8), 1557–1568 (2006)CrossRef Henderson, G.: Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Trans. Biomed. Eng. 53(8), 1557–1568 (2006)CrossRef
21.
Zurück zum Zitat Zhao, P., Ifeachor, E.: EEG assessment of Alzheimers diseases using universal compression algorithm. In: Proceedings of the 3rd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007), Plymouth, UK, 25 July 2007 Zhao, P., Ifeachor, E.: EEG assessment of Alzheimers diseases using universal compression algorithm. In: Proceedings of the 3rd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007), Plymouth, UK, 25 July 2007
22.
Zurück zum Zitat Ochoa, J.B.: EEG signal classification for brain computer interface applications. Ec. Polytech. Federale de Lausanne 7, 1–72 (2002) Ochoa, J.B.: EEG signal classification for brain computer interface applications. Ec. Polytech. Federale de Lausanne 7, 1–72 (2002)
23.
Zurück zum Zitat Guérit, J.: EEG and evoked potentials in the intensive care unit. Neurophysiol. Clin. Clin. Neurophysiol. 29(4), 301–317 (1999)CrossRef Guérit, J.: EEG and evoked potentials in the intensive care unit. Neurophysiol. Clin. Clin. Neurophysiol. 29(4), 301–317 (1999)CrossRef
24.
Zurück zum Zitat Moretti, D.: Quantitative EEG markers in mild cognitive impairment: degenerative versus vascular brain impairment. Int. J. Alzheimer’s Dis. 2012, 12 (2012) Moretti, D.: Quantitative EEG markers in mild cognitive impairment: degenerative versus vascular brain impairment. Int. J. Alzheimer’s Dis. 2012, 12 (2012)
25.
Zurück zum Zitat Moretti, D.: Vascular damage and EEG markers in subjects with mild cognitive impairment. Clin. Neurophysiol. 118(8), 1866–1876 (2007)CrossRef Moretti, D.: Vascular damage and EEG markers in subjects with mild cognitive impairment. Clin. Neurophysiol. 118(8), 1866–1876 (2007)CrossRef
26.
Zurück zum Zitat Pizzagalli, D.A.: Electroencephalography and high-density electrophysiological source localization. In: Handbook of Psychophysiology, vol. 3, pp. 56–84 (2007) Pizzagalli, D.A.: Electroencephalography and high-density electrophysiological source localization. In: Handbook of Psychophysiology, vol. 3, pp. 56–84 (2007)
27.
Zurück zum Zitat John, E.: Developmental equations for the electroencephalogram. Science 210(4475), 1255–1258 (1980)CrossRef John, E.: Developmental equations for the electroencephalogram. Science 210(4475), 1255–1258 (1980)CrossRef
28.
Zurück zum Zitat Jeong, J.: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115(7), 1490–1505 (2004)CrossRef Jeong, J.: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115(7), 1490–1505 (2004)CrossRef
29.
Zurück zum Zitat Taywade, S., Raut, R.: A review: EEG signal analysis with different methodologies. In: Proceedings of the National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) (2014) Taywade, S., Raut, R.: A review: EEG signal analysis with different methodologies. In: Proceedings of the National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) (2014)
30.
Zurück zum Zitat Husain, A., Tatum, W., Kaplan, P.: Handbook of EEG Interpretation. Demos Medical, New York (2008) Husain, A., Tatum, W., Kaplan, P.: Handbook of EEG Interpretation. Demos Medical, New York (2008)
31.
Zurück zum Zitat Punapung, A., Tretriluxana, S., Chitsakul, K.: A design of configurable ECG recorder module. In: Biomedical Engineering International Conference (BMEiCON). IEEE (2012) Punapung, A., Tretriluxana, S., Chitsakul, K.: A design of configurable ECG recorder module. In: Biomedical Engineering International Conference (BMEiCON). IEEE (2012)
32.
Zurück zum Zitat Klem, G.H.: The Ten-Twenty Electrode System of the International Federation Klem, G.H.: The Ten-Twenty Electrode System of the International Federation
33.
Zurück zum Zitat Anderson, C.W., Sijercic, Z.: Classification of EEG signals from four subjects during five mental tasks. In: Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks (EANN 1996), Turkey (1996) Anderson, C.W., Sijercic, Z.: Classification of EEG signals from four subjects during five mental tasks. In: Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks (EANN 1996), Turkey (1996)
34.
Zurück zum Zitat Müller, T.: Selecting relevant electrode positions for classification tasks based on the electro-encephalogram. Med. Biol. Eng. Compu. 38(1), 62–67 (2000)CrossRef Müller, T.: Selecting relevant electrode positions for classification tasks based on the electro-encephalogram. Med. Biol. Eng. Compu. 38(1), 62–67 (2000)CrossRef
35.
Zurück zum Zitat Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Chichester (2013) Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Chichester (2013)
36.
Zurück zum Zitat Moretti, D.V.: Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin. Neurophysiol. 115(2), 299–308 (2004)CrossRef Moretti, D.V.: Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin. Neurophysiol. 115(2), 299–308 (2004)CrossRef
37.
Zurück zum Zitat Jung, T.P.: Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin. Neurophysiol. 111(10), 1745–1758 (2000)CrossRef Jung, T.P.: Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin. Neurophysiol. 111(10), 1745–1758 (2000)CrossRef
38.
Zurück zum Zitat Núñez, I.M.B.: EEG Artifact Detection (2011) Núñez, I.M.B.: EEG Artifact Detection (2011)
39.
Zurück zum Zitat Guerrero-Mosquera, C., Trigueros, A.M., Navia-Vazquez, A.: EEG Signal Processing for Epilepsy, in Epilepsy-Histological, Electroencephalographic and Psychological Aspects, InTech (2012) Guerrero-Mosquera, C., Trigueros, A.M., Navia-Vazquez, A.: EEG Signal Processing for Epilepsy, in Epilepsy-Histological, Electroencephalographic and Psychological Aspects, InTech (2012)
40.
Zurück zum Zitat Molina, G.N.G.: Direct brain-computer communication through scalp recorded EEG signals. École Polytechnique Fedérale de Lausanne (2004) Molina, G.N.G.: Direct brain-computer communication through scalp recorded EEG signals. École Polytechnique Fedérale de Lausanne (2004)
41.
Zurück zum Zitat Naït-Ali, A.: Advanced Biosignal Processing. Springer Science & Business Media, Berlin (2009)CrossRef Naït-Ali, A.: Advanced Biosignal Processing. Springer Science & Business Media, Berlin (2009)CrossRef
42.
Zurück zum Zitat McKeown, M.: A new method for detecting state changes in the EEG: exploratory application to sleep data. J. Sleep Res. 7(S1), 48–56 (1998)CrossRef McKeown, M.: A new method for detecting state changes in the EEG: exploratory application to sleep data. J. Sleep Res. 7(S1), 48–56 (1998)CrossRef
43.
Zurück zum Zitat Zikov, T.: A wavelet based denoising technique for ocular artifact correction of the electroencephalogram. In: Proceedings of the Second Joint Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference. IEEE (2002) Zikov, T.: A wavelet based denoising technique for ocular artifact correction of the electroencephalogram. In: Proceedings of the Second Joint Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference. IEEE (2002)
44.
Zurück zum Zitat Krishnaveni, V.: Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J. Neural Eng. 3(4), 338 (2006)CrossRef Krishnaveni, V.: Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J. Neural Eng. 3(4), 338 (2006)CrossRef
45.
Zurück zum Zitat Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, JVA 2006. IEEE (2006) Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, JVA 2006. IEEE (2006)
46.
Zurück zum Zitat Akhtar, M.T., James, C.J.: Focal artifact removal from ongoing EEG–a hybrid approach based on spatially-constrained ICA and wavelet denoising. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009. IEEE (2009) Akhtar, M.T., James, C.J.: Focal artifact removal from ongoing EEG–a hybrid approach based on spatially-constrained ICA and wavelet denoising. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009. IEEE (2009)
47.
Zurück zum Zitat Inuso, G.: Wavelet-ICA methodology for efficient artifact removal from electroencephalographic recordings. In: International Joint Conference on Neural Networks, IJCNN 2007. IEEE (2007) Inuso, G.: Wavelet-ICA methodology for efficient artifact removal from electroencephalographic recordings. In: International Joint Conference on Neural Networks, IJCNN 2007. IEEE (2007)
48.
Zurück zum Zitat Jelles, B.: Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clin. Neurophysiol. 119(4), 837–841 (2008)CrossRef Jelles, B.: Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clin. Neurophysiol. 119(4), 837–841 (2008)CrossRef
49.
Zurück zum Zitat Escudero, J.: Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings: application to Alzheimer’s disease. Med. Eng. Phys. 31(7), 872–879 (2009)CrossRef Escudero, J.: Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings: application to Alzheimer’s disease. Med. Eng. Phys. 31(7), 872–879 (2009)CrossRef
50.
Zurück zum Zitat Hornero, R.: Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer’s disease. IEEE Trans. Biomed. Eng. 55(6), 1658–1665 (2008)CrossRef Hornero, R.: Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer’s disease. IEEE Trans. Biomed. Eng. 55(6), 1658–1665 (2008)CrossRef
51.
Zurück zum Zitat Markand, O.N.: Organic brain syndromes and dementias. Curr. Pract. Clin. Electroencephalogr. 3, 378–404 (1990) Markand, O.N.: Organic brain syndromes and dementias. Curr. Pract. Clin. Electroencephalogr. 3, 378–404 (1990)
52.
Zurück zum Zitat Dauwels, J., Vialatte, F., Cichocki, A.: Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr. Alzheimer Res. 7(6), 487–505 (2010)CrossRef Dauwels, J., Vialatte, F., Cichocki, A.: Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr. Alzheimer Res. 7(6), 487–505 (2010)CrossRef
53.
54.
Zurück zum Zitat Subha, D.P.: EEG signal analysis: a survey. J. Med. Syst. 34(2), 195–212 (2010)CrossRef Subha, D.P.: EEG signal analysis: a survey. J. Med. Syst. 34(2), 195–212 (2010)CrossRef
55.
Zurück zum Zitat Abásolo, D.: 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)CrossRef Abásolo, D.: 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)CrossRef
56.
Zurück zum Zitat Escudero, J.: Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol. Meas. 27(11), 1091 (2006)CrossRef Escudero, J.: Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol. Meas. 27(11), 1091 (2006)CrossRef
57.
59.
Zurück zum Zitat Hamadicharef, B.: Performance evaluation and fusion of methods for early detection of Alzheimer disease. In: International Conference on BioMedical Engineering and Informatics, BMEI 2008. IEEE (2008) Hamadicharef, B.: Performance evaluation and fusion of methods for early detection of Alzheimer disease. In: International Conference on BioMedical Engineering and Informatics, BMEI 2008. IEEE (2008)
60.
Zurück zum Zitat Henderson, G.T.: Early Detection of Dementia Using The Human Electroencephalogram (2004) Henderson, G.T.: Early Detection of Dementia Using The Human Electroencephalogram (2004)
61.
Zurück zum Zitat Ferenets, R.: Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans. Biomed. Eng. 53(6), 1067–1077 (2006)CrossRef Ferenets, R.: Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans. Biomed. Eng. 53(6), 1067–1077 (2006)CrossRef
62.
Zurück zum Zitat Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71(2), 021906 (2005)MathSciNetCrossRef Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71(2), 021906 (2005)MathSciNetCrossRef
63.
Zurück zum Zitat Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010)CrossRef Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010)CrossRef
64.
Zurück zum Zitat KavitaMahajan, M., Rajput, M.S.M.: A comparative study of ANN and SVM for EEG classification. Int. J. Eng. Res. Technol. IJERT 1, 1–6 (2012)CrossRef KavitaMahajan, M., Rajput, M.S.M.: A comparative study of ANN and SVM for EEG classification. Int. J. Eng. Res. Technol. IJERT 1, 1–6 (2012)CrossRef
65.
Zurück zum Zitat Vialatte, F.: Blind source separation and sparse bump modelling of time frequency representation of eeg signals: new tools for early detection of Alzheimer’s disease. In: IEEE Workshop on Machine Learning for Signal Processing. IEEE (2005) Vialatte, F.: Blind source separation and sparse bump modelling of time frequency representation of eeg signals: new tools for early detection of Alzheimer’s disease. In: IEEE Workshop on Machine Learning for Signal Processing. IEEE (2005)
66.
Zurück zum Zitat Besserve, M.: Classification methods for ongoing EEG and MEG signals. Biol. Res. 40(4), 415–437 (2007)CrossRef Besserve, M.: Classification methods for ongoing EEG and MEG signals. Biol. Res. 40(4), 415–437 (2007)CrossRef
67.
Zurück zum Zitat Garrett, D.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 141–144 (2003)CrossRef Garrett, D.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 141–144 (2003)CrossRef
68.
Zurück zum Zitat Lehmann, C.: Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J. Neurosci. Methods 161(2), 342–350 (2007)CrossRef Lehmann, C.: Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J. Neurosci. Methods 161(2), 342–350 (2007)CrossRef
Metadaten
Titel
The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia
verfasst von
Luis A. Guerra
Laura C. Lanzarini
Luis E. Sánchez
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-78605-6_11

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