CFP last date
20 December 2024
Reseach Article

A Survey of Detection of Cognitive Impairment using Non-invasive Indicators

by Shridevi Karande, Vrushali Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 14
Year of Publication: 2020
Authors: Shridevi Karande, Vrushali Kulkarni
10.5120/ijca2020920043

Shridevi Karande, Vrushali Kulkarni . A Survey of Detection of Cognitive Impairment using Non-invasive Indicators. International Journal of Computer Applications. 176, 14 ( Apr 2020), 1-6. DOI=10.5120/ijca2020920043

@article{ 10.5120/ijca2020920043,
author = { Shridevi Karande, Vrushali Kulkarni },
title = { A Survey of Detection of Cognitive Impairment using Non-invasive Indicators },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2020 },
volume = { 176 },
number = { 14 },
month = { Apr },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number14/31266-2020920043/ },
doi = { 10.5120/ijca2020920043 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:29.153690+05:30
%A Shridevi Karande
%A Vrushali Kulkarni
%T A Survey of Detection of Cognitive Impairment using Non-invasive Indicators
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 14
%P 1-6
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cognitive Impairment is a stage where a person faces difficulty in processing information, remembering, learning new things, concentrating or making decisions affecting their day to day life. These impairments range from mild to severe stage and in the long term they lead to Dementia and Alzheimer's disease. A person may have natural decline of cognition over the age and the nature of impairment changes from person to person. It’s important to detect and measure these changes from time to time. The heterogeneous nature of Cognitive impairment and the natural decline of cognition over the age makes its detection more difficult. The review presented here is a study of various non-invasive methods such as neuropsychological tests, speech and eye dynamics used to measure cognitive and behavioural changes. These methods are used in preclinical frontline screening and diagnosis of impairment and they have their have their own relative accuracy when used separately. This review explores a multi-modal approach of combining these cognitive and behavioural markers to improve detection accuracy.

References
  1. Global Health and Aging - World Health Organization http://www.who.int/ageing/publications/global_health.pdf
  2. World Alzheimer Report 2019: Attitudes to dementia. London: Alzheimer’s Disease International https://www.alz.co.uk/research/WorldAlzheimerReport2019.pdfhttps://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf
  3. Dauwels J, Kannan S. “Diagnosis of Alzheimer’s disease using electric signals of the brain. A grand challenge”, AsiaPacific Biotech News 2012;16(10n11):22–38, http://dx.doi.org/10.1142/S0219030312000651http://www.asiabiotech.com/publication/apbn/16/english/preserveddocs/1610n11/1610n11.pdf
  4. Breda Cullen, Brian O’Neill, Jonathan J Evans, Robert F Coen, Brian A Lawlor ,“A review of screening tests for cognitive impairment,” J Neurol Neurosurg Psychiatry 2007;78:790–799. doi: 10.1136/jnnp.2006.095414.
  5. James E. Galvin, “Using Informant and Performance Screening Methods to Detect Mild Cognitive Impairment and Dementia”, Springer Science and Business Media, LLC, part of Springer Nature 2018, 26 January 2018.
  6. William Rodman Shankle1, Subramani Mani, Michael J. Pazzani and Padhraic Smyth2,”Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods”.
  7. Sid E. O’Bryant, Joy D. Humphreys,,“Detecting Dementia With the Mini-Mental State Examination in Highly Educated Individuals”, ARCH NEUROL/VOL 65 (NO. 7), JULY 2008.
  8. M. F. Folstein, S. E. Folstein, and P. R. McHugh, “Mini-mental state'. A practical method for grading the cognitive state of patients for the clinician,'' J. Psychiatric Res., vol. 12, no. 3, pp. 189198, 1975.
  9. Natalia Ciesielska, Remigiusz Sokołowski, Ewelina Mazur, Marta Podhorecka, Anna Polak-Szabela, Kornelia Kędziora-Kornatowska,"Is the Montreal Cognitive Assessment (MoCA) test better suited than the Mini-Mental State Examination (MMSE) in mild cognitive impairment (MCI) detection among people aged over 60? Meta-analysis",J. Psychiatr. Pol. 2016; 50(5):1039–1052,DOI: https://doi.org/10.12740/PP/45368
  10. Andrew J. Larner, “Cognitive Screening Instruments for the diagnosis of mild cognitive Impairment”, Progress in Neurology and Psychiatry March/April 2016.
  11. Nina Kachiyanys, Kye kim, "Mini mental status examination mapping to corresponding brain areas in dementia " , Applied Technologies and Innovation Journal, Vol. 7, Issue 2, pp. 55to58. http://dx.doi.org/10.15208/ati.2012.7
  12. Nasreddine ZS, Phillips NA, Bédirian V, et al.The Montreal Cognitive Assessment, MoCA: abrief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53:695–9.
  13. Occas Pap R Coll Gen Pract," Abbreviated Mental Test Score (AMTS)", 1993 Apr; (59): 28.
  14. O'Caoimh, R. 2015. The Quick Mild Cognitive Impairment (Qmci) screen: developing a new screening test for mild cognitive impairment and dementia. PhD Thesis, University College Cork.
  15. Delnaz Palseia, G. Prasad Rao, Sarvada C. Tiwari1, Pragya Lodha2, Avinash De Sousa, “The Clock Drawing Test versus Mini mental Status Examination as a Screening Tool for Dementia: A Clinical Comparison ”,Indian Journal of psychological Medicine Psychiatric Society - South Zonal Branch, DOI:10.4103/IJPSYM.IJPSYM_244_17,2018.
  16. Berit A, OVE DEHLJN, “REVIEW The clock drawing Test. Age and Ageing 1998; 27:399-403.
  17. Rodolfo B. Ladeira, Breno S. Diniz, Paula V. Nunes, Orestes V. Forlenza , “ Combining Cognitive Screening Tests for the evaluation of MCI in the elderly '‘, clinics2009 doi: 10.1590/S1807-59322009001000006.
  18. Royall DR, Cordes JA, Polk M. CLOX: an executive clock drawing test. J Neurol Neurosurg Psychiatry 1998; 64 :588-94
  19. C Mittal, Col SP Gorthi, Maj Gen S Rohatgi, VSM, “Early Cognitive Impairment: Role of Clock Drawing Test”, MJAFI Elsevier 2010.
  20. Jesus Cachoa, Julian BenitoLe,onb, Ricardo Garc ıaGarc,ıad, Bernardino FernandezCalvoe,Jose Luis VicenteVillard, onf and Alex J. Mitchellg, “Does the Combination of the MMSE and Clock Drawing Test (MiniClock) Improve the Detection of Mild Alzheimer’s Disease and Mild Cognitive Impairment?”, Journal of Alzheimer’s Disease 22 (2010) 889–896 889,DOI 10.3233/JAD2010101182.
  21. Elisabete Pinto Ruth Peters, “Literature Review of the Clock DrawingTest as a Tool for Cognitive Screening”, Dement Geriatr Cogn Disord 2009;27:201–213,DOI: 10.1159/000203344
  22. Bárbara Costa Beber1, Renata Kochhann1,2, Bruna Matias1, Márcia Lorena Fagundes Chaves1,3, “The Clock Drawing Test Performance differences between the free-drawn and incomplete-copy versions in patients with MCI and dementia” pubmed Dement Neuropsychol. 2016 Jul-Sep;10(3):227-231 Berit A. The clock drawing Test. Age and Ageing 1998; 27 :399-403.
  23. K. L. de Ipi ˜na, J.-B. Alonso, C.M. Travieso, J. Sol-Casals, H. Egiraun,M. Faundez-Zanuy, A. Ezeiza, N. Barroso, M. Ecay-Torres,P. Martinez-Lage, and U. M. de Lizardui, “On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis,” Sensors, vol. 13, no. 5, pp. 6730–6745, 2013.
  24. [11b] A. König et al., “Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease,'' Alzheimer's Dementia, Diagnosis, Assessment Disease Monitor., vol. 1, no. 1, pp. 112124, 2015.
  25. B. Roark, M. Mitchell, J.-P. Hosom, K. Hollingshead, and J. Kaye, “Spoken language derived measures for detecting mild cognitive impairment,'‘ IEEE Trans. Audio, Speech, Language Process., vol. 19, no. 7,pp. 20812-090, Sep. 2011.
  26. Leandro B. dos Santos1, Edilson A. Corrˆea Jr1, Osvaldo N. Oliveira Jr2, Diego R. Amancio1,Let´ıcia L. Mansur3, Sandra M. Alu´ısio, “Enriching Complex Networks withWord Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts”, arXiv:1704.08088v1 [cs.CL] 26 Apr 2017
  27. L. T´oth, G. Gosztolya, V. Vincze, I. Hoffmann, G. Szatl´oczki, E. Bir´o, F. Zsura, M. P´ak´aski, and J. K´alm´an, “Automatic detection of mild cognitive impairment from spontaneous speech using ASR,” in Proceedings of Interspeech, Dresden, Germany, Sep 2015, pp. 2694–2698.
  28. Nikhil Yadav,Christian Poellabauer,Louis Daudet, “Portable Neurological Disease Assessment UsingTemporal Analysis of Speech”, BCB’15, September 09–12, 2015, Atlanta, GA, USA.
  29. E. Aramaki, S. Shikata, M. Miyabe, and A. Kinoshita, “Vocabulary size in speech may be an early indicator of cognitive impairment,'' PloS ONE, vol. 11, no. 5, p. E0155-195, 2016.
  30. S. O. Orimaye, J. S.-M. Wong, and K. J. Golden, “Learning predictive linguistic features for Alzheimer's disease and related dementias using verbal utterances”, Association for Computational Linguistics , 2014.
  31. Jarrold, Bart Peintner, David Wilkins, Dimitra Vergryi and Colleen Richey, Maria Luisa Gorno-Tempini and Jennifer Ogar, “Aided Diagnosis of Dementia Type through Computer-Based Analysis of Spontaneous Speech”, Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 27–37, Baltimore, Maryland USA, June 27, 2014
  32. K. C. Fraser, J. A. Meltzer, and F. Rudzicz, “Linguistic features identify Alzheimer's disease in narrative speech,'' J. Alzheimer's Disease, vol. 49, no. 2, pp. 407422, 2015.
  33. G´abor Gosztolya 1,2, L´aszl´o T´oth 1, Tam´as Gr´osz 2, Veronika Vincze 1,Ildik´o Hoffmann 34, Gr´eta Szatl´oczki 5, Magdolna P´ak´aski 5, J´anos K´alm´an 5, “Detecting Mild Cognitive Impairment from Spontaneous Speech by Correlation-Based Phonetic Feature Selection”, INTERSPEECH 2016 September 8–12, 2016, San Francisco, USA.
  34. J. F. Cohn et al., "Detecting depression from facial actions and vocal prosody", Proc. 3rd Int. Conf. Affective Comput. Intell. Interact. Workshops (ACII), pp. 1-7, Sep. 2009.
  35. H Tanaka, H Adachi, N Ukita, M Ikeda, H Kazui, T kudo, and S Nakamura, “Detecting Dementia Through Interactive Computer Avatars”, IEEE journal of Translational Engineering in Health and Medicine, 2017.
  36. Ane Alberdi Aramendi, Asier Aztiria, Adrian Basarab, “On the early diagnosis of Alzheimer ’s disease from multimodal signals: A survey”, Artificial Intelligence in Medicine, vol. 71. pp. 1-29. ISSN 0933-3657,2016
  37. S C Tiwari, Rakesh Kumar Tripathi Aditya Kumar, ‘‘ Applicability of the Mini-mental State Examination (MMSE) and the Hindi Mental State Examination (HMSE) to the urban elderly in India: A pilot study, December 2008International Psychogeriatrics 21(1):123-8 DOI: 10.1017/S1041610208007916
  38. Anderson TJ, MacAskill MR., “Eye movements in patients with neurodegenerative disorders. Nat Rev Neurol 2013;9(2):74–85,http://dx.doi.org/10.1038/nrneurol.2012.273 http://www.ncbi.nlm.nih.gov/pubmed/23338283
  39. Gerardo Fernandez, Pablo Mandolesi, Nora P Rot- ´ stein, Oscar Colombo, Osvaldo Agamennoni, and Luis E Politi., “Eye movement alterations during reading in patients with early Alzheimer disease”, Investigative ophthalmology & Visual Science,2013 54(13):8345–8352.
  40. Marta LG Pereira, Marina von Zuben A Camargo, Ivan Aprahamian, and Orestes V Forlenza, “Eye movement analysis and cognitive processing: detecting indicators of conversion to Alzheimer’s disease”,Neuropsychiatric Disease and Treatment, 2014,10:1273–1285.
  41. Juan Biondi, Gerardo Fernandez, Silvia Castro, and Osvaldo Agamenonni. 2017. Eye-movement behavior identification for AD diagnosis. arXiv preprint arXiv:1702.0083
  42. Kathleen C. Fraser1 , Kristina Lundholm Fors1 , Dimitrios Kokkinakis1 , Arto Nordlund2 “An analysis of eye-movements during reading for the detection of mild cognitive impairment“, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1016–1026 Copenhagen, Denmark, September 7–11, 2017. C 2017 Association for Computational Linguistics.
  43. M. Hoque, R. W. Picard, "Acted vs. Natural frustration and delight: Many people smile in natural frustration", Proc. IEEE Int. Conf. Autom. Face Gesture Recognit. Workshops (FG), pp. 354-359, Mar. 2011.
  44. K. Asplund, A. Norberg, R. Adolfsson, H. M. Waxman, "Facial expressions in severely demented patients—A stimulus–response study of four patients with dementia of the Alzheimer type", Int. J. Geriatric Psychiatry, vol. 6, no. 8, pp. 599-606, 1991.
  45. V. E. Sturm et al., "Mutual gaze in Alzheimer’s disease frontotemporal and semantic dementia couples", Social Cognit. Affective Neurosci., vol. 6, no. 3, pp. 359-367, 2010.
  46. A. Shimokawa et al., "Influence of deteriorating ability of emotional comprehension on interpersonal behavior in alzheimer-type dementia", Brain cognition, vol. 47, no. 3, pp. 423-433, 2001.
  47. D. Fernandez-Duque, S. E. Black, "Impaired recognition of negative facial emotions in patients with frontotemporal dementia", Neuropsychologia, vol. 43, no. 11, pp. 1673-1687, 2005.
  48. M. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, "Coding facial expressions with Gabor wavelets", Proc. 3rd IEEE Int. Conf. Autom. Face Gesture Recognit., pp. 200-205, Apr. 1998.
  49. J. F. Cohn et al., "Detecting depression from facial actions and vocal prosody", Proc. 3rd Int. Conf. Affective Comput. Intell. Interact. Workshops (ACII), pp. 1-7, Sep. 2009.
  50. Tim J. Anderson and Michael R. MacAskill, “Eye movements in patients with neurodegenerative disorders”, Macmillan Publishers Limited, VOL 9. FEBRUARY 2013
  51. R. J. Molitor, P. C. Ko, and B. A. Ally, “Eye movements in Alzheimer’s disease,” Journal of Alzheimers Disease, vol. 44, no. 1, pp. 1–12, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Dementia Alzheimer's disease(AD) Neuropsychological Assessment Cognitive domain.