{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:50:10Z","timestamp":1768809010385,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Agency for Medical Research and Development (AMED)","award":["22he2002025j0002"],"award-info":[{"award-number":["22he2002025j0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The number of people with dementia is increasing each year, and early detection allows for early intervention and treatment. Since conventional screening methods are time-consuming and expensive, a simple and inexpensive screening is expected. We created a standardized intake questionnaire with thirty questions in five categories and used machine learning to categorize older adults with moderate and mild dementia and mild cognitive impairment, based on speech patterns. To evaluate the feasibility of the developed interview items and the accuracy of the classification model based on acoustic features, 29 participants (7 males and 22 females) aged 72 to 91 years were recruited with the approval of the University of Tokyo Hospital. The MMSE results showed that 12 participants had moderate dementia with MMSE scores of 20 or less, 8 participants had mild dementia with MMSE scores between 21 and 23, and 9 participants had MCI with MMSE scores between 24 and 27. As a result, Mel-spectrogram generally outperformed MFCC in terms of accuracy, precision, recall, and F1-score in all classification tasks. The multi-classification using Mel-spectrogram achieved the highest accuracy of 0.932, while the binary classification of moderate dementia and MCI group using MFCC achieved the lowest accuracy of 0.502. The FDR was generally low for all classification tasks, indicating a low rate of false positives. However, the FNR was relatively high in some cases, indicating a higher rate of false negatives.<\/jats:p>","DOI":"10.3390\/s23115346","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T04:34:51Z","timestamp":1685939691000},"page":"5346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9827-9924","authenticated-orcid":false,"given":"Toshiharu","family":"Igarashi","sequence":"first","affiliation":[{"name":"Department of Human and Engineered Environmental Studies, The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa 277-8563, Japan"}]},{"given":"Yumi","family":"Umeda-Kameyama","sequence":"additional","affiliation":[{"name":"Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 3-1, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan"}]},{"given":"Taro","family":"Kojima","sequence":"additional","affiliation":[{"name":"Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 3-1, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan"}]},{"given":"Masahiro","family":"Akishita","sequence":"additional","affiliation":[{"name":"Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 3-1, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan"}]},{"given":"Misato","family":"Nihei","sequence":"additional","affiliation":[{"name":"Department of Human and Engineered Environmental Studies, The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa 277-8563, Japan"},{"name":"Institute of Gerontology, The University of Tokyo, 3-1, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","article-title":"The diagnosis of dementia due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"McKhann","year":"2011","journal-title":"Alzheimers Dement."},{"key":"ref_2","unstructured":"Barnhill, J.W. (2013). Kaplan\u2019s Textbook of Clinical Psychiatry Development of DSM-5 Diagnostic Criteria into Clinical Practice, American Psychiatric Pub.. [3rd ed.]. Chapter 21.3."},{"key":"ref_3","unstructured":"Fukuda, D. (2015). Development of a Nursing Intervention Program to Enhance Executive Function of People with Early Dementia. [Ph.D. Dissertation, University of Tsukuba]."},{"key":"ref_4","first-page":"487","article-title":"Prevalence of Dementia","volume":"71","author":"Asada","year":"2016","journal-title":"Curr. Med. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1016\/S0140-6736(13)60630-3","article-title":"Genetics of dementia","volume":"383","author":"Loy","year":"2014","journal-title":"Lancet"},{"key":"ref_6","unstructured":"Prince, M.J., Wimo, A., Guerchet, M.M., Ali, G.C., Wu, Y.T., and Prina, M. (2023, May 14). World Alzheimer Report 2015-The Global Impact of Dementia: An Analysis of Prevalence, Incidence, Cost and Trends. Available online: https:\/\/hal.science\/hal-03495438\/."},{"key":"ref_7","unstructured":"Umphred, D. Neurological Rehabilitation, Elsevier Mosby. [6th ed.]."},{"key":"ref_8","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Ageing 2019: Highlights, United Nations. ST\/ESA\/SER.A\/430."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/nrd3505","article-title":"The amyloid cascade hypothesis for Alzheimer\u2019s disease: An appraisal for the development of therapeutics","volume":"10","author":"Karran","year":"2011","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1016\/S0140-6736(17)31363-6","article-title":"Dementia prevention, intervention, and care","volume":"390","author":"Livingston","year":"2017","journal-title":"Lancet"},{"key":"ref_11","unstructured":"Ministry of Health, Labour and Welfare (2019). White Paper on Aging Society in Fiscal, Ministry of Health, Labour and Welfar."},{"key":"ref_12","unstructured":"Tsuchida, K. (2010). Background of the Shortage of Nursing Care Personnel in the Welfare Field. Bull. Kawasaki Med. Coll., 16, Available online: https:\/\/cir.nii.ac.jp\/crid\/1390290699747300480."},{"key":"ref_13","unstructured":"(2023, May 14). Tokyo Metropolitan Government Basic Survey on Welfare and Health, Bureau of Social Welfare and Health, Tokyo Metropolitan Government, Basic Survey on Welfare and Health, 2010. 17. Available online: https:\/\/www.fukushihoken.metro.tokyo.lg.jp\/kiban\/chosa_tokei\/zenbun\/heisei22\/index.html."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.jalz.2005.11.002","article-title":"Positron emission tomography and magnetic resonance imaging in the diagnosis and prediction of dementia","volume":"2","author":"Jagust","year":"2006","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_15","first-page":"CD010783","article-title":"Mini-Mental State Examination (MMSE) for the detection of Alzheimer\u2019s disease and other dementias in people with mild cognitive impairment (MCI)","volume":"3","author":"Smailagic","year":"2015","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_16","first-page":"1339","article-title":"Development of the revised Hasegawa brief intelligence rating scale (HDS-R)","volume":"2","author":"Shinji","year":"1991","journal-title":"Geriatr. Psychiatr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1177\/1471301218800122","article-title":"Understanding public-stigma and self-stigma in the context of dementia: A systematic review of the global literature","volume":"19","author":"Nguyen","year":"2020","journal-title":"Dementia"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1212\/WNL.50.2.546","article-title":"Prevalence and correlates of the catastrophic reaction in Alzheimer\u2019s disease","volume":"50","author":"Tiberti","year":"1998","journal-title":"Neurology"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kramer, G.P., Douglas, A.B., and Vicky, P. (2019). Introduction to Clinical Psychology, Cambridge University Press.","DOI":"10.1017\/9781108593823"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.pec.2008.10.010","article-title":"Communication: Observational Study: Patient initiation of information: Exploring its role during the mental health intake visit","volume":"75","author":"Nakash","year":"2009","journal-title":"Patient Educ. Couns."},{"key":"ref_21","unstructured":"Nakagawa, K., Shinosawa, K., Matsumura, R., Ishiguro, H., and Hagita, N. (2019). Persuasion Effect by Adding Personality to a Health Care Robot. Proc. Forum Inf. Sci. Technol., 9."},{"key":"ref_22","first-page":"8","article-title":"The word list learning test as an effective screening tool for dementia in Japanese older adults","volume":"36","author":"Renato","year":"2021","journal-title":"Arch. Clin. Neuropsychol."},{"key":"ref_23","first-page":"106124","article-title":"Use of language processing tasks to diagnose mild cognitive impairment: A non-machine learning approach","volume":"93","author":"Oveisgharan","year":"2021","journal-title":"J. Commun. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1056\/NEJMp1311405","article-title":"New insights into the dementia epidemic","volume":"369","author":"Larson","year":"2013","journal-title":"N. Engl. J. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/JSTSP.2019.2952087","article-title":"A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders","volume":"14","author":"Voleti","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_26","first-page":"640","article-title":"The prosody of speech: Melody and rhythm","volume":"5","author":"Nooteboom","year":"1997","journal-title":"Handb. Phon. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1109\/TASL.2011.2112351","article-title":"Spoken Language Derived Measures for Detecting Mild Cognitive Impairment","volume":"19","author":"Roark","year":"2011","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_28","first-page":"112","article-title":"Automatic speech-based detection of mild cognitive impairment and Alzheimer\u2019s disease from spontaneous speech using lexical analysis and prosody","volume":"57","author":"Satt","year":"2019","journal-title":"Comput. Speech Lang."},{"key":"ref_29","first-page":"916356","article-title":"Automatic prosodic analysis to identify mild dementia","volume":"2015","author":"Kairuz","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1093\/brain\/awr205","article-title":"Voice processing in dementia: A neuropsychological and neuroanatomical analysis","volume":"134","author":"Hailstone","year":"2011","journal-title":"Brain"},{"key":"ref_31","first-page":"157","article-title":"Analysis of speech signal in patients with Alzheimer;s disease through measures of complexity and entropy","volume":"41","author":"Cogollor","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_32","unstructured":"Nakamura, T., Meguro, K., Saito, Y., Nakatsuka, M., and Yamaguchi, S. (2017, January 16\u201318). A convolutional neural network-based classification of dementia using a voice test. Proceedings of the 8th Augmented Human International Conference, Mountain View, CA, USA."},{"key":"ref_33","first-page":"10","article-title":"Classification of Alzheimer\u2019s disease using support vector machine with voice features","volume":"118","author":"Lin","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Horwitz-Martin, R.L., Quatieri, T.F., Lammert, A.C., Williamson, J.R., Yunusova, Y., Godoy, E., Mehta, D.D., and Green, J.R. (2016). Relation of Automatically Extracted Formant Trajectories with Intelligibility Loss and Speaking Rate Decline in Amyotrophic Lateral Sclerosis. Proc. Interspeech, 1205\u20131209.","DOI":"10.21437\/Interspeech.2016-403"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"EL477","DOI":"10.1121\/1.4826150","article-title":"Automatic assessment of vowel space area","volume":"134","author":"Sandoval","year":"2013","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_36","first-page":"1","article-title":"A large set of audio features for sound description (similarity and classification) in the CUIDADO project","volume":"54","author":"Peeters","year":"2004","journal-title":"CUIDADO Ist Proj. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Meghanani, A., Anoop, C.S., and Ramakrishnan, A.G. (2021, January 19\u201322). An exploration of log-mel spectrogram and MFCC features for Alzheimer\u2019s dementia recognition from spontaneous speech. Proceedings of the 2021 IEEE Spoken Language Technology Workshop (SLT), Shenzhen, China.","DOI":"10.1109\/SLT48900.2021.9383491"},{"key":"ref_38","first-page":"12145","article-title":"Speech emotion recognition using MFCC and convolutional neural network","volume":"79","author":"Ghosh","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_39","first-page":"479","article-title":"Mel-frequency spectrogram-based cough sound recognition","volume":"15","author":"Zhu","year":"2021","journal-title":"IET Signal Process."},{"key":"ref_40","unstructured":"Rabiner, L.R., and Juang, B.H. (1993). Fundamentals of Speech Recognition, Pearson Education India."},{"key":"ref_41","first-page":"43124","article-title":"An end-to-end convolutional neural network for sound classification","volume":"9","author":"Han","year":"2021","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Luz, S., Haider, F., de la Fuente, S., Fromm, D., and MacWhinney, B. (2020, January 25\u201329). Alzheimer\u2019s dementia recognition through spontaneous speech: The ADReSS Challenge. Proceedings of the INTERSPEECH 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2571"},{"key":"ref_43","unstructured":"Shibata, D., Ito, K., Shoji, W., and Osamu, A. (2019). Construction of a Corpus of Elderly People with Control Groups and Development of a Screening Technique for Preliminary Dementia Using the Corpus. Trans. Jpn. Soc. Artif. Intell., 34."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Igarashi, T., and Nihei, M. (2022). Cognitive Assessment of Japanese Older Adults with Text Data Augmentation. Healthcare, 10.","DOI":"10.3390\/healthcare10102051"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1017\/S1041610217001740","article-title":"Conversational assessment of cognitive dysfunction among residents living in long-term care facilities","volume":"30","author":"Oba","year":"2017","journal-title":"Int. Psychogeriatrics"},{"key":"ref_46","unstructured":"(2023, February 07). Available online: https:\/\/gopro.com."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.jamda.2008.05.006","article-title":"Cognitive screening for dementia and mild cognitive impairment in assisted living: Comparison of 3 tests","volume":"9","author":"Kaufer","year":"2008","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3810\/pgm.2009.03.1990","article-title":"Computer Assessment of Mild Cognitive Impairment","volume":"121","author":"Saxton","year":"2009","journal-title":"Postgrad. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"Psychiatr. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1001\/jamainternmed.2015.2152","article-title":"Cognitive Tests to Detect Dementia: A Systematic Review and Metaanalysis","volume":"175","author":"Tsoi","year":"2015","journal-title":"JAMA Intern. Med."},{"key":"ref_51","first-page":"165","article-title":"Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version","volume":"5","author":"Sheikh","year":"1986","journal-title":"Clin. Gerontol. J. Aging Ment. Health"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/07317115.2016.1199452","article-title":"A Validity and Reliability Study of the Japanese Version of the Geriatric Depression Scale 15 (GDS-15-J)","volume":"40","author":"Sugishita","year":"2017","journal-title":"Clin. Gerontol."},{"key":"ref_53","unstructured":"(2023, February 07). Available online: https:\/\/librosa.org\/doc\/latest\/index.html."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_55","unstructured":"Bonaventure, F.P.D., and Yeno, K.S.G. (2021, January 11\u201317). Deep Convolutional Neural Networks for Speech Emotion Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, Montreal, BC, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5346\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:48:25Z","timestamp":1760125705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,5]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115346"],"URL":"https:\/\/doi.org\/10.3390\/s23115346","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,5]]}}}