Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network

Authors

  • G. Anuradha Department of ECE, BSA Crescent Institute of Science and Technology, India
  • D. N. Jamal Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, India

Abstract

Dementia has become a global public health issue. The current study is focused on diagnosing dementia with Electro Encephalography (EEG). The detection of the advancement of the disease is carried out by detecting the abnormal behavior in EEG measurements. Assessment and evaluation of EEG abnormalities is conducted for all the subjects in order to detect dementia. EEG feature analysis, namely dominant frequency, dominant frequency variability, and frequency prevalence, is done for abnormal and normal subjects and the results are compared. For dementia with Lewy bodies, in 85% of the epochs, the dominant frequency is present in the delta range whereas for normal subjects it lies in the alpha range. The dominant frequency variability in 75% of the epochs is above 4Hz for dementia with Lewy bodies, and in normal subjects at 72% of the epochs, the dominant frequency variability is less than 2Hz. It is observed that these features are sufficient to diagnose dementia with Lewy bodies. The classification of Lewy body dementia is done by using a feed-forward artificial neural network wich proved to have a 94.4% classification accuracy. The classification with the proposed feed-forward neural network has better accuracy, sensitivity, and specificity than the already known methods.

Keywords:

Lewy body dementia, EEG, dementia, neural network, dominant frequency

Downloads

Download data is not yet available.

References

J. Wu, Y. Yu, and J. Yang, "Neuropsychological Parameters as Potential Biomarkers for Alzheimer’s Disease," Current Translational Geriatrics and Experimental Gerontology Reports, vol. 1, no. 2, pp. 68–75, Jun. 2012. DOI: https://doi.org/10.1007/s13670-012-0007-4

S. Fahn, "Description of Parkinson’s disease as a clinical syndrome," Annals of the New York Academy of Sciences, vol. 991, pp. 1–14, Jun. 2003. DOI: https://doi.org/10.1111/j.1749-6632.2003.tb07458.x

P. M. Rossini, S. Rossi, C. Babiloni, and J. Polich, "Clinical neurophysiology of aging brain: From normal aging to neurodegeneration," Progress in Neurobiology, vol. 83, no. 6, pp. 375–400, Dec. 2007. DOI: https://doi.org/10.1016/j.pneurobio.2007.07.010

R. C. Petersen et al., "Apolipoprotein E Status as a Predictor of the Development of Alzheimer’s Disease in Memory-Impaired Individuals," Journal of the American Medical Informatics Association, vol. 273, no. 16, pp. 1274–1278, Apr. 1995. DOI: https://doi.org/10.1001/jama.273.16.1274

C. Flicker, S. H. Ferris, and B. Reisberg, "Mild cognitive impairment in the elderly: Predictors of dementia," Neurology, vol. 41, no. 7, pp. 1006–1006, Jul. 1991. DOI: https://doi.org/10.1212/WNL.41.7.1006

J. Jeong, "EEG dynamics in patients with Alzheimer’s disease," Clinical Neurophysiology, vol. 115, no. 7, pp. 1490–1505, Jul. 2004. DOI: https://doi.org/10.1016/j.clinph.2004.01.001

G. Henderson et al., "Development and assessment of methods for detecting dementia using the human electroencephalogram," IEEE Transactions on Biomedical Engineering, vol. 53, no. 8, pp. 1557–1568, Aug. 2006. DOI: https://doi.org/10.1109/TBME.2006.878067

Y. E. Geda, "Mild Cognitive Impairment in Older Adults," Current Psychiatry Reports, vol. 14, no. 4, pp. 320–327, Aug. 2012. DOI: https://doi.org/10.1007/s11920-012-0291-x

M. P. Mattson, "Pathways towards and away from Alzheimer’s disease," Nature, vol. 430, no. 7000, pp. 631–639, Aug. 2004. Cedazo-Minguez and B. Winblad, "Biomarkers for Alzheimer’s disease and other forms of dementia: Clinical needs, limitations and future aspects," Experimental Gerontology, vol. 45, no. 1, pp. 5–14, Jan. 2010. DOI: https://doi.org/10.1016/j.exger.2009.09.008

B. Reisberg, S. H. Ferris, M. J. de Leon, and T. Crook, "Global Deterioration Scale (GDS)," Psychopharmacology Bulletin, vol. 24, no. 4, pp. 661–663, 1988.

R. C. Petersen, G. E. Smith, S. C. Waring, R. J. Ivnik, E. G. Tangalos, and E. Kokmen, "Mild Cognitive Impairment: Clinical Characterization and Outcome," Archives of Neurology, vol. 56, no. 3, pp. 303–308, Mar. 1999. DOI: https://doi.org/10.1001/archneur.56.3.303

P. Luu, D. M. Tucker, R. Englander, A. Lockfeld, H. Lutsep, and B. Oken, "Localizing Acute Stroke-related EEG Changes:: Assessing the Effects of Spatial Undersampling," Journal of Clinical Neurophysiology, vol. 18, no. 4, pp. 302–317, Jul. 2001. DOI: https://doi.org/10.1097/00004691-200107000-00002

P. D. Meek, E. K. McKeithan, and G. T. Schumock, "Economic Considerations in Alzheimer’s Disease," Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, vol. 18, no. 2P2, pp. 68–73, 1998. DOI: https://doi.org/10.1002/j.1875-9114.1998.tb03880.x

J. T. Coyle, D. L. Price, and M. R. DeLong, "Alzheimer’s disease: a disorder of cortical cholinergic innervation," Science, vol. 219, no. 4589, pp. 1184–1190, Mar. 1983. DOI: https://doi.org/10.1126/science.6338589

A. V. Terry and J. J. Buccafusco, "The Cholinergic Hypothesis of Age and Alzheimer’s Disease-Related Cognitive Deficits: Recent Challenges and Their Implications for Novel Drug Development," Journal of Pharmacology and Experimental Therapeutics, vol. 306, no. 3, pp. 821–827, Sep. 2003. DOI: https://doi.org/10.1124/jpet.102.041616

C. McVeigh and P. Passmore, "Vascular dementia: prevention and treatment," Clinical Interventions in Aging, vol. 1, no. 3, pp. 229–235, Sep. 2006. DOI: https://doi.org/10.2147/ciia.2006.1.3.229

G. C. Roman, "Vascular Dementia: Distinguishing Characteristics, Treatment, and Prevention," Journal of the American Geriatrics Society, vol. 51, no. 5s2, pp. S296–S304, 2003. DOI: https://doi.org/10.1046/j.1532-5415.5155.x

D. R. Thal, L. T. Grinberg, and J. Attems, "Vascular dementia: Different forms of vessel disorders contribute to the development of dementia in the elderly brain," Experimental Gerontology, vol. 47, no. 11, pp. 816–824, Nov. 2012. DOI: https://doi.org/10.1016/j.exger.2012.05.023

H. Hampel et al., "Perspective on future role of biological markers in clinical therapy trials of Alzheimer’s disease: A long-range point of view beyond 2020," Biochemical Pharmacology, vol. 88, no. 4, pp. 426–449, Apr. 2014. DOI: https://doi.org/10.1016/j.bcp.2013.11.009

U. P. Mosimann and I. G. McKeith, "Dementia with lewy bodies--diagnosis and treatment," Swiss medical weekly, vol. 133, no. 9–10, pp. 131–142, Mar. 2003.

L. A. Coben, W. L. Danziger, and L. Berg, "Frequency analysis of the resting awake EEG in mild senile dementia of Alzheimer type," Electroencephalography and Clinical Neurophysiology, vol. 55, no. 4, pp. 372–380, Apr. 1983. DOI: https://doi.org/10.1016/0013-4694(83)90124-4

L. A. Coben, W. Danziger, and M. Storandt, "A longitudinal EEG study of mild senile dementia of Alzheimer type: changes at 1 year and at 2.5 years," Electroencephalography and Clinical Neurophysiology, vol. 61, no. 2, pp. 101–112, Aug. 1985. DOI: https://doi.org/10.1016/0013-4694(85)91048-X

C. Babiloni et al., "Cortical sources of resting state electroencephalographic alpha rhythms deteriorate across time in subjects with amnesic mild cognitive impairment," Neurobiology of Aging, vol. 35, no. 1, pp. 130–142, Jan. 2014. DOI: https://doi.org/10.1016/j.neurobiolaging.2013.06.019

I. G. McKeith et al., "Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): Report of the consortium on DLB international workshop," Neurology, vol. 47, no. 5, pp. 1113–1124, Nov. 1996.

L. Bonanni, A. Thomas, P. Tiraboschi, B. Perfetti, S. Varanese, and M. Onofrj, "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, vol. 131, no. 3, pp. 690–705, Mar. 2008. DOI: https://doi.org/10.1093/brain/awm322

S.-C. Du, D.-L. Huang, and H. Wang, "An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology," IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 10, pp. 2590–2604, Oct. 2015. DOI: https://doi.org/10.1109/TIM.2015.2418684

N. N. Kulkarni and V. K. Bairagi, "Extracting Salient Features for EEG-based Diagnosis of Alzheimer’s Disease Using Support Vector Machine Classifier," IETE Journal of Research, vol. 63, no. 1, pp. 11–22, Jan. 2017. DOI: https://doi.org/10.1080/03772063.2016.1241164

S. Du, C. Liu, and L. Xi, "A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology," Journal of Manufacturing Science and Engineering, vol. 137, Feb. 2015, Art. no. 011003. DOI: https://doi.org/10.1115/1.4028165

L. Bonanni et al., "Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies," Neurobiology of Aging, vol. 36, no. 1, pp. 434–445, Jan. 2015. DOI: https://doi.org/10.1016/j.neurobiolaging.2014.07.009

R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state," Physical Review E, vol. 64, no. 6, Nov. 2001, Art. no. 061907. DOI: https://doi.org/10.1103/PhysRevE.64.061907

"EEG time series download page," Department of Epileptology, University of Bonn. http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/eegdata.html (accessed Mar. 24, 2021).

G. Anuradha, N. Jamal, and S. Rafiammal, "Detection of dementia in EEG signal using dominant frequency analysis," in IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, Chennai, India, Sep. 2017, pp. 710–714. DOI: https://doi.org/10.1109/ICPCSI.2017.8391806

M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, Nov. 1994. DOI: https://doi.org/10.1109/72.329697

G. Fiscon et al., "Combining EEG signal processing with supervised methods for Alzheimer’s patients classification," BMC Medical Informatics and Decision Making, vol. 18, no. 1, May 2018, Art. no. 35. DOI: https://doi.org/10.1186/s12911-018-0613-y

L. R. Trambaiolli, A. C. Lorena, F. J. Fraga, P. A. M. Kanda, R. Anghinah, and R. Nitrini, "Improving Alzheimer’s Disease Diagnosis with Machine Learning Techniques," Clinical EEG and Neuroscience, vol. 42, no. 3, pp. 160–165, Jul. 2011. DOI: https://doi.org/10.1177/155005941104200304

S. S. Rafiammal, D. N. Jamal, and S. K. Mohideen, "Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5803–5807, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3419

M. B. Ayed, "Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6462–6468, Dec. 2020. DOI: https://doi.org/10.48084/etasr.3878

Downloads

How to Cite

[1]
Anuradha, G. and Jamal, D.N. 2021. Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network. Engineering, Technology & Applied Science Research. 11, 3 (Jun. 2021), 7135–7139. DOI:https://doi.org/10.48084/etasr.4112.

Metrics

Abstract Views: 637
PDF Downloads: 557

Metrics Information

Most read articles by the same author(s)