Detection of mild cognitive impairment based on mouse movement data of trail making test

Guerchet, 2020 Prince, 2016, Recent global trends in the prevalence and incidence of dementia, and survival with dementia, Alzheimers Res Ther, 8, 1, 10.1186/s13195-016-0188-8 Bai, 2022, Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies, Age Ageing, 51 Chehrehnegar, 2020, Early detection of cognitive disturbances in mild cognitive impairment: a systematic review of observational studies, Psychogeriatrics, 20, 212, 10.1111/psyg.12484 Sperling, 2014, The evolution of preclinical Alzheimer’s disease: implications for prevention trials, Neuron, 84, 608, 10.1016/j.neuron.2014.10.038 Rasmussen, 2019, Alzheimer’s disease–why we need early diagnosis, Degener Neurol Neuromuscul Dis, 9, 123 Livingston, 2020, Dementia prevention, intervention, and care: 2020 report of the Lancet Commission, Lancet, 396, 413, 10.1016/S0140-6736(20)30367-6 Brookmeyer, 2007, Forecasting the global burden of Alzheimer’s disease, Alzheimers Dement, 3, 186, 10.1016/j.jalz.2007.04.381 Oliveira, 2014, Computerized Neurocognitive Test (CNT) in mild cognitive impairment and Alzheimer’s disease, Dement Neuropsychol, 8, 112, 10.1590/S1980-57642014DN82000005 Égerházi, 2007, Automated Neuropsychological Test Battery (CANTAB) in mild cognitive impairment and in Alzheimer’s disease, Prog Neuropsychopharmacol Biol Psychiatry, 31, 746, 10.1016/j.pnpbp.2007.01.011 Snyder, 2011, Assessment of cognition in mild cognitive impairment: a comparative study, Alzheimers Dement, 7, 338, 10.1016/j.jalz.2011.03.009 Lussier, 2019, Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: A systematic review, IEEE J Biomed Health Inform, 23, 838, 10.1109/JBHI.2018.2834317 Khan, 2021, Prediction of mild cognitive impairment using movement complexity, IEEE J Biomed Health Inform, 25, 227, 10.1109/JBHI.2020.2985907 Ghoraani, 2021, Detection of mild cognitive impairment and Alzheimer’s disease using dual-task gait assessments and machine learning, Biomed Signal Process Control, 64, 10.1016/j.bspc.2020.102249 Lagun, 2011, Detecting cognitive impairment by eye movement analysis using automatic classification algorithms, J Neurosci Methods, 201, 196, 10.1016/j.jneumeth.2011.06.027 Stringer, 2018, Can you detect early dementia from an email? A proof of principle study of daily computer use to detect cognitive and functional decline, Int J Geriatr Psychiatry, 33, 867, 10.1002/gps.4863 Seelye, 2015, Computer mouse movement patterns: A potential marker of mild cognitive impairment, Alzheimers Dement (Amst), 1, 472, 10.1016/j.dadm.2015.09.006 Arevalo-Rodriguez, 2015, Mini-mental state examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI) Alladi, 2006, Mild cognitive impairment: applicability of research criteria in a memory clinic and characterization of cognitive profile, Psychol Med, 36, 507, 10.1017/S0033291705006744 Crawford, 2012, A systematic review of the accuracy and clinical utility of the Addenbrooke’s cognitive examination and the Addenbrooke’s cognitive examination—Revised in the diagnosis of dementia, Int J Geriatr Psychiatry, 27, 659, 10.1002/gps.2771 Stanczak, 1998, The expanded trail making test: rationale, development, and psychometric properties, Arch Clin Neuropsychol, 13, 473, 10.1093/arclin/13.5.473 Petersen, 2009, Mild cognitive impairment: ten years later, Arch Neurol, 66, 1447, 10.1001/archneurol.2009.266 Rey, 1964 Ferreira Correia, 2013, The Rey Auditory Verbal Learning Test: Normative Data Developed for the Venezuelan Population, Arch Clin Neuropsychol, 29, 206, 10.1093/arclin/act070 Hall, 2010, 7 - neuropsychology, 121 Kónya, 1995 Petersen, 2004, Mild cognitive impairment as a diagnostic entity, J Intern Med, 256, 183, 10.1111/j.1365-2796.2004.01388.x Csukly, 2016, The differentiation of amnestic type MCI from the non-amnestic types by structural MRI, Front Aging Neurosci, 8, 52, 10.3389/fnagi.2016.00052 Chawla, 2002, SMOTE: synthetic minority over-sampling technique, J Artif Intell Res, 16, 321, 10.1613/jair.953 Press, 1990, Savitzky-Golay smoothing filters, Comput Phys, 4, 669, 10.1063/1.4822961 McKelvey, 2018, Interpretable machine learning in healthcare, IEEE Intell Inform Bull, 19 Malik, 2015, ICDAR2015 competition on signature verification and writer identification for on-and off-line skilled forgeries (SigWIcomp2015), 1186 Ful̈öp, 2016