Amnesia, commonly referred to as Alzheimer’s, is a type of brain dysfunction that gradually dissipates the patient’s mental abilities. Memory disorder usually develops gradually and progresses. At first, memory impairment is limited to recent events and lessons, but old memories are gradually damaged. In this disease, the connection between nerve cells by the formation of neurofibrillary nodes disappeared. Currently, treatment for the disease mainly involves symptomatic treatments, treatment of behavioral disorders and medication use. Although there is no cure for Alzheimer's disease yet, medications can slow the progression of the disease and reduce the severity of memory impairment and behavioral problems. Today, whit the spread of definitive treatment for this disease, in this study, new techniques for the treatment of this disease can be explored by examining the early detection methods of the disease through brain signal processing with classifiers and medical imaging such as MRI and CT Scan. Signal processing has included EEG and ERP brain signals and the use of classifiers such as SVM, LDA and Neural network. In medical image processing, a combination of Neural network and Wavelet is used to expedite the time of diagnosis according to the above method. Given the process under consideration, combining brain signals and medical imaging can provide valuable help in early detection of Alzheimer disease.
| Published in | Bioprocess Engineering (Volume 4, Issue 1) |
| DOI | 10.11648/j.be.20200401.13 |
| Page(s) | 17-22 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Alzheimer's Disease, Image and Signal Processing, Classifiers, Neural Network, Wavelet
| [1] | Pedro Miguel Rodrigues, Joã Paulo Teixeira, Carolina Garrett, Dílio Alves and Diamantino Freitas, Conference on Enterprise. Alzheimer’s Early Prediction with Electroencephalogram. Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / Proj MAN / HCist 2016, October 5-7, 2016. |
| [2] | Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E. Alzheimer's disease. The Lancet 2011; 377: 1019-1031. |
| [3] | Rodrigues P., Teixeira J. P. Alzheimer’s Disease Recognition with Artificial Neural Networks, In Information Systems and Technologies for Enhancing Health and Social Care, by R. Martinho, R. Rijo, M. Cruz-Cunha and J. Varajão (Eds.), IGI Global 2013, 102-118, 2013. |
| [4] | Rodrigues P, Teixeira J. Artificial neural networks in the discrimination of alzheimer's disease. Communications in Computer and Information Science 2011; 221: 272-281. |
| [5] | Rodrigues, P. M., Freitas, D. R. and Teixeira, J. P. Alzheimer’s electroencephalogram event scalp localization”, IEEE 9th International Workshop on Multidimensional (nD) Systems (nDS); Vila Real, 2015, 1-4. |
| [6] | Akrofi, K., Pal, R., Baker, R. C., Nutter, B. S. and Schiffer, R. W. Classification of Alzheimer's disease and mild cognitive impairment by pattern recognition of EEG power and coherence, Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, Dallas, 2010, 606-609. |
| [7] | O Pogarell, S J Teipel, G Juckel, L Gootjes, T Mo¨ller, K Bu¨rger, G Leinsinger, H-J Mo¨ller, U Hegerl, H Hampel. EEG coherence reflects regional corpus callosum area in Alzheimer’s disease. J Neurol Neurosurg Psychiatry 2005; 76: 109–111. doi: 10.1136/jnnp.2004.036566. |
| [8] | Francesco Carlo Morabito, Senior Member, IEEE, Domenico Labate, Alessia Bramanti, Fabio La Foresta, Member, IEEE, Giuseppe Morabito, Isabella Palamara, and Harold H. Szu, Fellow, IEEE. Enhanced Compressibility of EEG Signal in Alzheimer’s Disease Patients. IEEE SENSORS JOURNAL, VOL. 13, NO. 9, SEPTEMBER 2013. |
| [9] | A. J. Casson, D. C. Yates, S. J. Smith, J. S. Duncan, and E. RodriguezVillegas, “Wearable electroencephalography,” IEEE Eng. Med. Biol. Mag., vol. 29, no. 3, pp. 44–56, May–Jun. 2010. |
| [10] | D. Labate, I. Palamara, G. Occhiuto, G. Morabito, F. La Foresta, and F. C. Morabito, “Complexity analysis of Alzheimer’s disease EEG data through multiscale permutation entropy,” in Proc. 7th Int. Workshop Biosignal Interpretat., 2012, pp. 185–188. |
| [11] | J. Dauwels, K. Srinivasan, M. R. Reddy, T. Musha, F.-B. Vialatte, C. Latchoumane, J. Jeong, and A. Cichocki, “Slowing and loss of complexity in Alzheimer’s EEG: Two sides of the same coin?” Int. J. Alzheimer’s Disease, vol. 2011, no. 539621, Feb. 2011. |
| [12] | Ruofan Wang, Jiang Wang, Haitao Yu, Xile Wei, Chen Yang, Bin Deng. Power spectral density and coherence analysis of Alzheimer’s EEG. Cogn Neurodyn (2015) 9: 291–304 DOI 10.1007/s11571-014-9325-x. |
| [13] | Dauwels J, Srinivasan K, Reddy MR, Cichocki A (2013) Nearlossless multichannel EEG compression based on matrix and tensor decompositions. IEEE J Biomed Health Inform 17 (3): 708–714. |
| [14] | Gallego-Jutgla ` E, Elgendi M, Vialatte F, Sole ´-Casals J, Cichocki A, Latchoumane C, Jeong J, Dauwels J (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. |
| [15] | Han CX, Wang J, Yi GS, Che YQ (2013) Investigation of EEG abnormalities in the early stage of Parkinson’s disease. Cogn Neurodyn 7 (4): 351–359. |
| [16] | Haleh Aghajani et al. “Diagnosis of early Alzheimer’s disease based on EEG source localization and a standardized realistic head model”. In: IEEEjournalofbiomedicalandhealthinformatics17.6 (2013), pp. 1039– 1045. |
| [17] | Kaj Blennow Colin L. Masters Randall Bateman. “Primer Alzheimer’s diseasel”. In: NatureReviewsDiseasePrimers1. 15056 (2015). |
| [18] | Justin Dauwels, Francois Vialatte, and Andrzej Cichocki“ Diagnosis of Alzheimer’s disease from EEG signals: where are westanding?” In: CurrentAlzheimerResearch7.6 (2010), pp. 487–505. |
| [19] | Gareth James et al. “Support vector machines”. In: An Introduction to StatisticalLearning. Springer, 2013, pp. 337–372. |
| [20] | Aunsia Khan and Muhammad Usman. “Early diagnosis of Alzheimer’s disease using machine learning techniques: A review paper”. In: Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), 20157thInternationalJointConferenceon. Vol. 1. IEEE. 2015, pp. 380–387. |
| [21] | Morabito FC, Campolo M, Labate D, Morabito G, Bonanno L, et al: A longitudinal EEG study of Alzheimer’s disease progression based on a complex network approach. Int J Neural Syst 2015; 25: 1550005. |
| [22] | M. Prince, R. Bryce, E. Albanese, A. Wimo, W. Ribeiro, and C. P. Ferri, “The global prevalence of dementia: a systematic review and metaanalysis.” Alzheimers Dement, vol. 9, no. 1, pp. 63–75. e2, 2013. |
| [23] | F. Falahati, E. Westman, and A. Simmons, “Multivariate Data Analysis and Machine Learning in Alzheimer’s Disease with a Focus on Structural Magnetic Resonance Imaging.” J Alzheimer Disease, vol. 41, no. 3, pp. 685–708, 2014. |
| [24] | M. Liu, D. Zhang, “Inherent Structure Based Multi-view Learning with Multi-template Feature Representation for Alzheimer’s Disease Diagnosis”, IEEE Transactions on Biomedical Engineering, vol. 63, pp. 1473-1482, 2015. |
| [25] | A. L. Spedding, G. Di Fatta, J. D. Saddy t, and ADNI, “An LDA and Probability-based Classifier for the Diagnosis of Alzheimer's Disease from Structural MRI” BIBM, IEEE International Conference on, pp. 1404-1411, 2015. |
| [26] | G. Lizarraga, M. Cabrerizo, “A Web Platform for Data Acquisition and Analysis for Alzheimer’s Disease”, SoutheastCon, pp. 1-5, 2016. |
| [27] | J. C. Daza, A. Rueda, “Classification of Alzheimer’s Disease in MRI using Visual Saliency Information”, IEEE 11th Colombian Computing Conference (CCC), pp. 1-7, 2016. |
| [28] | T. Li, W. Zhang, “Classification of brain disease from magnetic resonance images based on multi-level brain partitions”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5933-5936, 2016. |
| [29] | H. T. Gorji, J. Haddadnia “A novel method for early diagnosis of Alzheimer's Disease based on pseudo Zernike moment from structural MRI”, Neuroscience, pp. 361-31, 2015. |
| [30] | A. Demirhan, T. M. Nir, “Feature selection improves the accuracy of classifying Alzheimer's disease using diffusion tensor images”, IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 126130, 2015. |
| [31] | H. Matsuda. MRI morphometry in Alzheimer’s disease. Ageing research reviews. 2016; 30: 17-24. |
| [32] | Biju K S, Alfa S S, Kavya Lal, Alvia Antony, Akhil M Kurup. Alzheimer’s Detection Based on Segmentation of MRI Image. 7th International Conference on Advances in Computing & Communications, ICACC-2017, 22-24 August 2017, Cochin, India. |
| [33] | Elisa Canu, Federica Agosta, Gorana Mandi-Stojmenovica,, Tanja Stojković, Elka Stefanova, Alberto Inuggi, Francesca Imperiale, Massimiliano Copetti, Vladimir S. Kostic, Massimo Filippi. Multiparametric MRI to distinguish early onset Alzheimer's disease and behavioral variant of frontotemporal dementia. Neuro Image: Clinical 15 (2017) 428–438. |
| [34] | Johnson, K. A., Fox, N. C., Sperling, R. A., Klunk, W. E., 2012. Brain imaging in Alzheimer disease. Cold Spring Harb. Perspect. Med. 2. |
| [35] | Liu, F., Zhou, L., Shen, C., Yin, J., 2014. Multiple kernel learning in the primal for multimodal alzheimer's disease classification. IEEE J. Biomed. Health Inform. 18. |
| [36] | Ritter, K., Schumacher, J., Weygandt, M., Buchert, R., Allefeld, C., Haynes, J.-D., 2015. Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers. Alzheimer's Dement. Diagn. Assess. Dis. Monit. 1, 206–215. |
| [37] | Xia, Y., Lu, S., Wen, L., Eberl, S., Fulham, M., Feng, D. D., 2014. Automated identification of dementia using FDG-PET imaging. Biomed. Res. Int. 2014. |
| [38] | Ahmed, M. U.; Mandic, D. P. Multivariate multiscale entropy. IEEE Signal Process. Lett. 2012, 19, 91–95. |
| [39] | Morabito, G.; Bramanti, A.; Labate, D.; La Foresta, F.; Morabito, F. C. Early detection of Alzheimer’s onset with permutation entropy analysis of EEG. Nat. Intell. 2011, 1, 30–32. |
| [40] | Mizuno, T.; Takahashi, T.; Cho, R. Y.; Kikuchi, M.; Murata, T.; Takahashi, K. Assessment of EEG dynamical complexity in Alzheimer’s Disease using multiscale entropy. Clin. Neurophysiol. 2010, 27, 1091–1106. |
| [41] | Hu, M.; Liang, H. Adaptive multiscale entropy analysis of multivariate neural data. IEEE Trans. Biomed. Eng. 2012, 59, 12–15. |
| [42] | Dauwels, J.; Vialatte, F.; Cichocki, A. Diagnosis of Alzheimer’s Diseases from EEG Signals: Where Are We Standing? Curr. Alzheimer Res. 2010, 7, 487–505. |
| [43] | Yang, A. C.; Wang, S. J.; Lai, K. L.; Tsai, C. F.; Yang, C. H.; Hwang, J. P.; Lo, M. T.; Huang, N. E.; Peng, C. K.; Fuh, J. L. CognitiveandneuropsychiatriccorrelatesofEEGdynamiccomplexityinpatientswithAlzheimer’s disease. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2013, 47, 52–61. |
| [44] | Mcbride, J. C.; Zhao, X.; Munro, N. B.; Smith, C. D.; Jicha, G. A.; Hively, L. S.; Broster, L. S.; Schmitt, F. A.; Kryscio, R. J.; Jiang, Y. Spectral and Complexity Analysis of Scalp EEG Characteristics for Mild Cognitive Impairmentand Early Alzheimer’s Disease. Comput. Methods Programs Biomed. 2014, 114, 153–163. |
| [45] | Garn, H.; Waser, M.; Deistler, M.; Benke, T.; Dal-Bianco, P.; Ransmayr, G.; Schmidt, H.; Sanin, G.; Santer, P.; Caravias, G.; et al. Electroencephalographic Complexity Markers Explain Neuropsychological Test Scores in Alzheimer’s Disease. In Proceedings of the 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, Valencia, Spain, 1–4 June 2014. |
| [46] | Garn, H.; Waser, M.; Deistler, M.; Benke, T.; Dal-Bianco, P.; Ransmayr, G.; Schmidt, H.; Sanin, G.; Santer, P.; Caravias, G.; et al. Quantitative EEG markers relate to Alzheimer’s diseases severity in the Prospective Dementia Registry Austria (PRODEM). Clin. Neurophysiol. 2015, 126, 505–513. |
| [47] | Wu, S. D.; Wu, C. W.; Lee, K. Y.; Lin, S. G. Modified multiscale entropy for short-term time series analysis. Phys. A Stat. Methods Appl. 2013, 392, 5865–5873. |
| [48] | Kork, F.; Gentsch, A; Holthues, J.; Hellweg, R; Jankowski, V.; Tepel, M.; Zidek, W.; Jankowski, J. Abiomarker for severity of Alzheimer’s disease: H-NMR resonances in cerebrospinal fluid correlate with performance in mini-mental-state-exam. Biomarkers 2012, 17, 36–42. |
| [49] | N. N. Kulkarni & V. K. Bairagi. Extracting Salient Features for EEG-based Diagnosis of Alzheimer's Disease Using Support Vector Machine Classifier. ISSN: 0377-2063 (Print) 0974-780X (Online) Journal homepage. IETE JOURNAL OF RESEARCH, 2016. |
| [50] | N. Kulkarni and V. Bairagi, “Diagnosis of Alzheimer disease using EEG signals,” Int. J. Eng. Res. Technol. (IJERT), Vol. 3, no. 4, pp. 1835–8, Apr. 2014. |
| [51] | S. Du, D. Huang, and H. Wang, “An adaptive support vector machine – based workpiece surface classification system using high definition metrology,” IEEE Trans. Instrument. Meas., Vol. 64, no. 10, pp. 2590–604, 2015. |
APA Style
Elyas Irankhah. (2020). Evaluation of Early Detection Methods for Alzheimer's Disease. Bioprocess Engineering, 4(1), 17-22. https://doi.org/10.11648/j.be.20200401.13
ACS Style
Elyas Irankhah. Evaluation of Early Detection Methods for Alzheimer's Disease. Bioprocess Eng. 2020, 4(1), 17-22. doi: 10.11648/j.be.20200401.13
AMA Style
Elyas Irankhah. Evaluation of Early Detection Methods for Alzheimer's Disease. Bioprocess Eng. 2020;4(1):17-22. doi: 10.11648/j.be.20200401.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.be.20200401.13,
author = {Elyas Irankhah},
title = {Evaluation of Early Detection Methods for Alzheimer's Disease},
journal = {Bioprocess Engineering},
volume = {4},
number = {1},
pages = {17-22},
doi = {10.11648/j.be.20200401.13},
url = {https://doi.org/10.11648/j.be.20200401.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.be.20200401.13},
abstract = {Amnesia, commonly referred to as Alzheimer’s, is a type of brain dysfunction that gradually dissipates the patient’s mental abilities. Memory disorder usually develops gradually and progresses. At first, memory impairment is limited to recent events and lessons, but old memories are gradually damaged. In this disease, the connection between nerve cells by the formation of neurofibrillary nodes disappeared. Currently, treatment for the disease mainly involves symptomatic treatments, treatment of behavioral disorders and medication use. Although there is no cure for Alzheimer's disease yet, medications can slow the progression of the disease and reduce the severity of memory impairment and behavioral problems. Today, whit the spread of definitive treatment for this disease, in this study, new techniques for the treatment of this disease can be explored by examining the early detection methods of the disease through brain signal processing with classifiers and medical imaging such as MRI and CT Scan. Signal processing has included EEG and ERP brain signals and the use of classifiers such as SVM, LDA and Neural network. In medical image processing, a combination of Neural network and Wavelet is used to expedite the time of diagnosis according to the above method. Given the process under consideration, combining brain signals and medical imaging can provide valuable help in early detection of Alzheimer disease.},
year = {2020}
}
TY - JOUR T1 - Evaluation of Early Detection Methods for Alzheimer's Disease AU - Elyas Irankhah Y1 - 2020/02/04 PY - 2020 N1 - https://doi.org/10.11648/j.be.20200401.13 DO - 10.11648/j.be.20200401.13 T2 - Bioprocess Engineering JF - Bioprocess Engineering JO - Bioprocess Engineering SP - 17 EP - 22 PB - Science Publishing Group SN - 2578-8701 UR - https://doi.org/10.11648/j.be.20200401.13 AB - Amnesia, commonly referred to as Alzheimer’s, is a type of brain dysfunction that gradually dissipates the patient’s mental abilities. Memory disorder usually develops gradually and progresses. At first, memory impairment is limited to recent events and lessons, but old memories are gradually damaged. In this disease, the connection between nerve cells by the formation of neurofibrillary nodes disappeared. Currently, treatment for the disease mainly involves symptomatic treatments, treatment of behavioral disorders and medication use. Although there is no cure for Alzheimer's disease yet, medications can slow the progression of the disease and reduce the severity of memory impairment and behavioral problems. Today, whit the spread of definitive treatment for this disease, in this study, new techniques for the treatment of this disease can be explored by examining the early detection methods of the disease through brain signal processing with classifiers and medical imaging such as MRI and CT Scan. Signal processing has included EEG and ERP brain signals and the use of classifiers such as SVM, LDA and Neural network. In medical image processing, a combination of Neural network and Wavelet is used to expedite the time of diagnosis according to the above method. Given the process under consideration, combining brain signals and medical imaging can provide valuable help in early detection of Alzheimer disease. VL - 4 IS - 1 ER -
Email: