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Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques[*]A Preliminary Study Funding The present work was partially funded by the government of Galicia (Xunta de Galicia, Spain), which covered the travel expenses to participants’ homes during the pilot study (grant # 2016/236), and also by ‘Rede Galega de Investigación en Demencias’ (IN607C2017/02) funded by Axencia Galega de Innovación GAIN – Xunta de Galicia.
28 July 2017
accepted: 06 July 2018
24 September 2018 (online)
Objective: Alzheimer’s disease (AD) is one of the most prevalent diseases among the adult population. The early detection of Mild Cognitive Impairment (MCI), which may trigger AD, is essential to slow down the cognitive decline process.
Methods: This paper presents a suit of serious games that aims at detecting AD and MCI overcoming the limitations of traditional tests, as they are time-consuming, affected by confounding factors that distort the result and usually administered when symptoms are evident and it is too late for preventive measures. The battery, named Panoramix, assesses the main early cognitive markers (i.e., memory, executive functions, attention and gnosias). Regarding its validation, it has been tested with a cohort study of 16 seniors, including AD, MCI and healthy individuals.
Results: This first pilot study offered initial evidence about psychometric validity, and more specifically about construct, criterion and external validity. After an analysis using machine learning techniques, findings show a promising 100% rate of success in classification abilities using a subset of three games in the battery. Thus, results are encouraging as all healthy subjects were correctly discriminated from those already suffering AD or MCI.
Conclusions: The solid potential of digital serious games and machine learning for the early detection of dementia processes is demonstrated. Such a promising performance encourages further research to eventually introduce this technique for the clinical diagnosis of cognitive impairment.
Key wordsAlzheimer’s disease - early detection - machine learning - data analytics - serious games - Classification and Regression Trees - logistic regression - random forest - Support Vector Machine
* Supplementary material published on our website https://doi.org/10.3414/ME17-02-0011
- 1 Selnes OA. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary. Oxford: Oxford University Press; 1991
- 2 Williams W, Graham DP, McCurry K, Sanders A, Eiseman J, Chiu PH, King-Casas B. Group psychotherapy’s impact on trust in veterans with PTSD: a pilot study. Bull Menninger Clin 2014; 78 (04) 335-348.
- 3 Cordell CB, Borson S, Boustani M, Chodosh J, Reuben D, Verghese J, Thies W, Fried LB. Medicare Detection of Cognitive Impairment Workgroup. Alzheimer’s Association recommendations for operationalizing the detection of cognitive impairment during the Medicare Annual Wellness Visit in a primary care setting. Alzheimer’s Dement 2013; 9 (02) 141-150.
- 4 Holtzman DM, Morris JC, Goate AM. Alzheimer’s disease: the challenge of the second century. Sci Transl Med 2011; 3 (77) 77sr1.
- 5 Parsons TD. Virtual Teacher and Classroom for Assessment of Neurodevelopmental Disorders. In: Brooks A, Brahnam S, Jain L. editors. Technologies of Inclusive Well-Being. Berlin, Heidelberg: Springer; 2014. p. 121-137.
- 6 Luciana M. Practitioner review: computerized assessment of neuropsychological function in children: clinical and research applications of the Cambridge Neuropsychological Testing Automated Battery (CANTAB). J Child Psychol Psychiatry 2003; 44 (05) 649-663.
- 7 Hsu Y-L, Chung P-C, Wang W-H, Pai M-C, Wang C-Y, Lin C-W, Wu H-L, Wang J-S. Gait and balance analysis for patients with Alzheimer’s disease using an inertial-sensor-based wearable instrument. IEEE J Biomed Health Inform 2014; 18 (06) 1822-1830.
- 8 Michael DR, Chen SL. Serious games: Games that educate, train, and inform. Muska & Lipman/Premier-Trade; 2005
- 9 Nap HH, Diaz-Orueta U, González MF, LozarManfreda K, Facal D, Dolniɩar V, Oyarzun D, Ranga M-M, de Schutter B. Older people’s perceptions and experiences of a digital learning game. Gerontechnology 2014; 13 (03) 322-331.
- 10 Valladares-Rodriguez S, Pérez-Rodriguez R, Anido-Rifón L, Fernández-Iglesias M. Trends on the application of serious games to neuropsychological evaluation: A scoping review. J Biomed Inform 2016; 64: 296-319.
- 11 Plancher GG, Barra J, Orriols E, Piolino P. The influence of action on episodic memory: a virtual reality study. Q J Exp Psychol (Hove) 2013; 66 (05) 895-909.
- 12 Sauzéon H, Arvind Pala P, Larrue F, Wallet G, Déjos M, Zheng X, Guitton P, N’Kaoua B. The use of Virtual Reality for episodic memory assessment: Effects of active navigation. Exp Psychol 2012; 59 (02) 99-108.
- 13 Jebara N, Orriols E, Zaoui M, Berthoz A, Piolino P. Effects of enactment in episodic memory: a pilot virtual reality study with young and elderly adults. Front Aging Neurosci 2014; 6: 338.
- 14 Díaz-Orueta U, Garcia-López C, Crespo-Eguílaz N, Sánchez-Carpintero R, Climent G, Narbona J, Diaz-Orueta U, Garcia-Lopez C, Crespo-Eguilaz N, Sanchez-Carpintero R, Climent G, Narbona J. AULA virtual reality test as an attention measure: Convergent validity with Conners Continuous Performance Test. Child Neuropsychol 2014; 20 (03) 328-342.
- 15 Iriarte Y, Diaz-Orueta U, Cueto E, Irazustabarrena P, Banterla F, Climent G. AULA – Advanced virtual reality tool for the assessment of attention: Normative study in Spain. J Atten Disord 2016; 20 (06) 542-568.
- 16 Shriki L, Weizer M, Pollak Y, Weiss PL, Rizzo AA, Gross-Tsur V. The utility of a continuous performance test embedded in virtual reality in measuring the effectiveness of MPH treatment in boys with ADHD. Harefuah 2010; 149 (01) 18-23 63.
- 17 Lee J-Y, Kho S, Yoo H Bin, Park S, Choi J-S, Kwon JS, Cha KR, Jung H-Y. Spatial memory impairments in amnestic mild cognitive impairment in a virtual radial arm maze. Neuropsychiatr Dis Treat 2014; 10: 653-660.
- 18 Atkins SM, Sprenger AM, Colflesh GJ, Briner TL, Buchanan JB, Chavis SE, Chen SY, Iannuzzi GL, Kashtelyan V, Dowling E, Harbison JI, Bolger DJ, Bunting MF, Dougherty MR. Measuring working memory is all fun and games: A four-dimensional spatial game predicts cognitive task performance. Exp Psychol 2014; 61 (06) 417-438.
- 19 Hagler S, Jimison HB, Pavel M. Assessing executive function using a computer game: computational modeling of cognitive processes. IEEE J Biomed Health Inform 2014; 18 (04) 1442-1452.
- 20 Werner P, Rabinowitz S, Klinger E, Korczyn AD, Josman N. Use of the Virtual Action Planning Supermarket for the Diagnosis of Mild Cognitive Impairment. Dement Geriatr Cogn Disord 2009; 27: 301-309.
- 21 van der Ham IJM, Faber AME, Venselaar M, van Kreveld MJ, Löffler M. Ecological validity of virtual environments to assess human navigation ability. Front Psychol 2015; 6: 637.
- 22 Serino S, Morganti F, Di Stefano F, Riva G. Detecting early egocentric and allocentric impairments deficits in Alzheimer’s disease: An experimental study with virtual reality. Front Aging Neurosci 2015; 7: 88.
- 23 Canty AL, Fleming J, Patterson F, Green HJ, Man D, Shum DHK. Evaluation of a virtual reality prospective memory task for use with individuals with severe traumatic brain injury. Neuropsychol Rehabil 2014; 24 (02) 238-265.
- 24 Henry JD, Terrett G, Altgassen M, RaponiSaunders S, Ballhausen N, Schnitzspahn KM, Rendell PG. A Virtual Week study of prospective memory function in autism spectrum disorders. J Exp Child Psychol 2014; 127: 110-125.
- 25 Banville F, Nolin P, Lalonde S, Henry M, Dery M-P, Villemure R. Multitasking and prospective memory: Can virtual reality be useful for diagnosis?. Behav Neurol 2010; 23 (04) 209-211.
- 26 AERA, APA, NCME.. The Standards for Educational and Psychological Testing. AERA; 2014
- 27 Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256 (03) 183-194.
- 28 Facal D, Guàrdia-Olmos J, Juncos-Rabadán O. Diagnostic transitions in mild cognitive impairment by use of simple Markov models. Int J Geriatr Psychiatry 2015; 30 (07) 669-676.
- 29 Juncos-Rabadán O, Pereiro AX, Facal D, Rodriguez N, Lojo C, Caamaño JA, Sueiro J, Boveda J, Eiroa P. Prevalence and correlates of cognitive impairment in adults with subjective memory complaints in primary care centres. Dement Geriatr Cogn Disord 2012; 33 (04) 226-232.
- 30 Valladares-Rodriguez S, Perez-Rodriguez R, Facal D, Fernandez-Iglesias MJ, Anido-Rifon L, Mouriño-Garcia M. Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults. PeerJ 2017; 5: e3508.
- 31 Liamputtong P. Focus group methodology: Principle and practice. Sage Publications; 2011
- 32 UNESCO Institute for Statistics [Internet].. International Standard Classification of Education: ISCED 2011. UIS, Montreal, Quebec;: 2012. Available from: http://uis.unesco.org/sites/default/files/documents/international-standard-classificationof-education-isced-2011-en.pdf.
- 33 Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform 2014; 48: 193-204.
- 34 Suresh KP, Chandrashekara S. Sample size estimation and power analysis for clinical research studies. J Hum Reprod Sci 2012; 5 (01) 7.
- 35 Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 2012; 380 9836 37-43.
- 36 Soyka F, Giordano PR, Barnett-Cowan M, Bulthoff HH. Modeling direction discrimination thresholds for yaw rotations around an earth-vertical axis for arbitrary motion profiles. Exp Brain Res 2012; 220 (01) 89-99.
- 37 Cockrell JR, Folstein MF. Mini-Mental State Examination (MMSE). Psychopharmacol Bull 1987; 24 (04) 689-692.
- 38 Rami L, Bosch B, Sanchez-Valle R, Molinuevo JL. The memory alteration test ([email protected] T) discriminates between subjective memory complaints, mild cognitive impairment and Alzheimer’s disease. Arch Gerontol Geriatr 2010; 50 (02) 171-174.
- 39 Delis DC, Kramer JH, Kaplan E, Thompkins BAO. CVLT: California Verbal Learning Test-Adult Version: Manual. San Antonio, Texas: Psychological Corporation; 1987
- 40 Jorm AF. The Informant Questionnaire on cognitive decline in the elderly (IQCODE): a review. Int Psychogeriatrics 2004; 16 (03) 275-293.
- 41 IJsselsteijn WA, De Kort YAW, Poels K. The Game Experience Questionnaire: Development of a selfreport measure to assess the psychological impact of digital games. Forthcoming;
- 42 De’ath G, Fabricius KE. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 2000; 81 (11) 3178-3192.
- 43 Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes 2011; 4 (01) 299.
- 44 Lehmann C, Koenig T, Jelic V, Prichep L, John RE, Wahlund L-O, Dodge Y, Dierks T. Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J Neurosci Methods 2007; 161 (02) 342-350.
- 45 Archer KJ, Kimes RV. Empirical characterization of random forest variable importance measures. Comput Stat Data Anal 2008; 52 (04) 2249-2260.
- 46 Pal M. Random forest classifier for remote sensing classification. Int J Remote Sens 2005; 26 (01) 217-222.
- 47 Tulving E. Episodic memory: from mind to brain. Annu Rev Psychol 2002; 53 (01) 1-25.
- 48 Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment 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 2011; 7 (03) 270-279.
- 49 Serino S, Pedroli E, Cipresso P, Pallavicini F, Albani G, Mauro A, Riva G. The role of virtual reality in neuropsychology: The virtual multiple errands test for the assessment of executive functions in Parkinson’s disease. Intell Syst Ref Libr 2014; 68: 257-274.
- 50 Campos-Magdaleno M, Díaz-Bóveda R, JuncosRabadán O, Facal D, Pereiro AX. Learning and serial effects on verbal memory in mild cognitive impairment. Appl Neuropsychol Adult 2015; 23 (04) 1-14.
- 51 Howard D, Patterson KE. The Pyramids and Palm Trees Test: A test of semantic access from words and pictures. Thames Valley Test Company; 1992
- 52 Schacter DL, Tulving E. Memory systems 1994. ilustrada. Mit Press; 1994
- 53 Kaplan E, Fein D, Morris R, Delis D. WAIS-R NI Manual. San Antonio: Psychological Corporation; 1991
- 54 Schank RC, Abelson RP. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Psychology Press; 2013
- 55 Lezak MD. Neuropsychological assessment. Oxford: Oxford University Press; 2004
- 56 Gliem JA, Gliem RR. Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. In: Midwest Researchto-Practice Conference in Adult, Continuing, and Community Education;. The Ohio State University; Columbus: 2003
- 57 Plancher G, Tirard A, Gyselinck V, Nicolas S, Piolino P. Using virtual reality to characterize episodic memory profiles in amnestic mild cognitive impairment and Alzheimer’s disease: influence of active and passive encoding. Neuropsychologia 2012; 50 (05) 592-602.