Acoustic Analysis in Mild Cognitive Diagnosis: Systematic Review of the Literature in 2008–2020

DOI:  https://doi.org/10.12804/revistas.urosario.edu.co/revsalud/a.9877

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Published: Oct 4, 2021
Issue: Vol. 19 No. 3 (2021): Ciencias de la Salud
Section Clinical or Experimental Research


Keywords: Acoustics of language cognitive dysfunction speech voice

Main Article Content

Authors

  Camilo Rodriguez Fandiño
Universidad El Bosque (Colombia)
  Ana Maria Salazar Montes
Universidad El Bosque (Colombia)

Abstract

Introduction: In recent research, changes in the vocal tone and timbre production that occur in late adulthood have been described. These changes indicate early cognitive disturbances, even in preclinical stages of cognitive decline. This study aims to identify relevant findings from the literature regarding acoustic analysis in elderly adults with cognitive impairment. Material and methods: A systematic review study was conducted, in which the following databases were consulted: PlosOne, Science Direct, PubMed/PMC, and Google Scholar. Search engines such as acoustic analysis, Alzheimer’s disease, mild cognitive impairment, prosody, voice analysis, and voice production were used. Additionally, empirical articles describing the acoustic analysis in elderly adults with cognitive risk are included. The evaluation was independently performed by two evaluators, who determined the risk of bias in the review. A total of 59 articles related to the topic were found, of which 25 met the inclusion criteria. Results: The reviewed articles identified changes in linguistic and paralinguistic prosody, timbre, and vocal tonality, which are associated with cognitive decline in the elderly. Conclusion: Study protocols in the acoustic analysis could be a good tool to support the differential clinical diagnosis of cognitive deterioration in late adulthood and a good opportunity to identify the risk in preclinical stages of dementia.

How to Cite
Rodriguez Fandiño, C., & Salazar Montes, A. M. (2021). Acoustic Analysis in Mild Cognitive Diagnosis: Systematic Review of the Literature in 2008–2020. Revista Ciencias De La Salud, 19(3), 1–16. https://doi.org/10.12804/revistas.urosario.edu.co/revsalud/a.9877
References

Lekue Ma. Características generales del envejecimiento y las personas mayores. El Farmacéutico: Profesión y Cultura. 2018;(557):31-9.

Aranco N, Stampini M, Ibarrarán P, Medellín N. Panorama de envejecimiento y dependencia en América Latina y el Caribe. Washington: Inter-American Development Bank; 2018. http://dx.doi.org/10.18235/0000984

Salazar AM, Canal S, Cabrera E, Guzmán Y, Amarillo J. Guía práctica de evaluación neuropsicológica del adulto. En: Aula psicológica. 23.a ed. Bogotá: El Bosque; 2019. p. 17-32.

Organización Mundial de la Salud. Demencia [internet]. Ginebra; 2019 sep 19. Disponible en: https://www.who.int/es/news-room/fact-sheets/detail/dementia

Pose M, Manes F. Deterioro cognitivo leve. Acta Neurol Colomb. 2010;26(3):7-12.

Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, Jack CR. Mild cognitive impairment: ten years later. Arch Neurol. 2009;66(12):1447-55. https://doi.org/10.1001/ archneurol.2009.266

Dubois B, Feldman H, Jacova C, Hampel H, Molinuevo JL, Blennow K, Cappa S. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6):614-29. https://doi.org/10.1016/S1474-4422(14)70090-0

Mora S, García R, Perea MV, Ladera V, Unzueta J, Patino MC, Rodríguez E. Deterioro cog- nitivo leve: detección temprana y nuevas perspectivas. Rev Neurol. 2012;54(5):303-10. https://doi.org/10.33588/rn.5405.2011538

Alzheimer’s Association. Qué es el Alzheimer [internet]. 2019 mar 4. Disponible en: https://www.alz.org/alzheimer-demencia/que-es-la-enfermedad-de-alzheimer

Pírez G, Lobo A, Santabárbara J. Deterioro cognitivo leve: análisis de la prevalencia, conversión a demencia y mortalidad [tesis de doctorado]. Zaragoza: Universidad de Zaragoza; 2017. Disponible en URL: https://zaguan.unizar.es/record/63065/files/ TESIS-2017-084.pdf

Katz MJ, Lipton RB, Hall CB, Zimmerman ME, Sanders AE, Verghese J, Derby CA. Age and sex-specific prevalence and incidence of mild cognitive impairment, dementia and Alzheimer’s dementia in black sand whites: are port from the Einstein Aging Study. Alzheimer DisAssoc Disord. 2012;26(4):335-345. https://doi.org/10.1097/WAD.0b013e31823dbcfc

Albert MS, Dekosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Snyder PJ. 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. Alzheimer's Dement. 2011;7(3):270-9. https:// doi.org/10.1016/j.jalz.2011.03.008

König A, Satt A, Sorin A, Hoory R, Toledo O, Derreumaux A, et al. Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimers Dement (Amst). 2015;1(1):112-24. https://doi.org/10.1016/j.dadm.2014.11.012

Tóth L, Gosztolya G, Vincze V, Hoffmann I, Szatlóczki G, Biró E, Kálmán J. Automatic detection of mild cognitive impairment from spontaneous speech using ASR. Documento procedente de la Sixteenth Annual Conference of the International Speech Communication Association; 2015.

Linz N, Tröger J, Alexandersson J, Wolters M, König A, Robert P. Predicting dementia screening and staging scores from semantic verbal fluency performance. Documento procedente de 2017 IEEE International Conference on Data Mining Workshops (ICDMW) IEEE. 2017;719-728. https://doi.org/10.1109/ICDMW.2017.100

Linz N, Klinge X, Tröger J, Alexandersson J, Zeghari R, Philippe R, König A. Automatic detection of apathy using acoustic markers extracted from free emotional speech. Documento procedente del 2nd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living; 2018.

Tröger J, Linz N, König A, Robert P, Alexandersson J. Telephone-based Dementia Screening I: automated semantic verbal fluency assessment. Documento procedente del 2th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM. 2018;59-66. https://doi.org/10.1145/3240925.3240943

Orimaye SO, Wong JSM, Wong CP. Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia. PloS One. 2018;13(11). https://doi.org/10.1371/journal.pone.0205636

Khodabakhsh A, Yesil F, Guner E, Demiroglu C. Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech. J Audio Speech Music Proc. 2015;9(1). https://doi.org/10.1186/s13636-015-0052-y

Roark B, Mitchell M, Hosom JP, Hollingshead K, Kaye J. Spoken language derived measures for detecting mild cognitive impairment. IEEE Transac Audio Speech Lang Proc. 2011;19(7):2081-90. https://doi.org/10.1109/TASL.2011.2112351

Aimoni C, Prosser S, Ciorba A, Menozzi L, Soavi C, Zuliani G. Speech audiometry tests in noise are impaired in older patients with mild cognitive impairment: a pilot study. J Int Adv Otol. 2014;(10)3:228-33. https://doi.org/10.5152/iao.2014.349

Hayashi A, Nomura H, Mochizuki R, Ohnuma A, Kimpara T, Ootomo K, Mori E. Neural substrates for writing impairments in Japanese patients with mild Alzheimer’s disease: A SPECT study. Neuropsychologia. 2011;49(7):1962-8. https://doi.org/10.1016/j.neuropsychologia.2011.03.024

Hayashi A, Nomura H, Mochizuki R., Ohnuma A, Kimpara T, Suzuki K, et al. Writing impairments in Japanese patients with mild cognitive impairment and with mild Alzheimer’s disease. Dement Geriatr Cogn Disord Extra. 2015;5(3):309-19. https://doi. org/10.1159/000437297

López-De-Ipiña K, Alonso JB, Travieso C, Solé-Casals J, Egiraun H, Faundez-Zanuy M, et al. On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors. 2013;13(5):6730-45. https://doi.org/10.3390/ s130506730

König A, Linz N, Tröger J, Wolters M, Alexandersson J, Robert P. Fully automatic speech-based analysis of the semantic verbal fluency task. Dement Geriatr Cogn Disord. 2018;45(3-4):198-209. https://doi.org/10.1159/000487852

König A, Satt A, Sorin A, Hoory R, Derreumaux A, David R, Robert PH. Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people. Curr Alzheimer Res. 2018;15(2):120-9. https://doi.org/10.2174/15672050 14666170829111942

Meilán JJ, Martínez F, Carro J, López DE, Millian-Morell L, Arana JM. Speech in Alzheimer’s disease: can temporal and acoustic parameters discriminate dementia? Dement Geriatr Cogn Disord. 2014;37(5-6):327-34. https://doi.org/10.1159/000356726

Meilán JJ, Martínez F, Carro J, Carcavilla N, Ivanova O. Voice markers of lexical access in mild cognitive impairment and Alzheimer’s disease. Curr Alzheimer Res. 2018;15(2):111- 9. https://doi.org/10.2174/1567205014666170829112439

König A, Satt A, David R, Robert P. Innovative voice analytics for the assessment and monitoring of cognitive decline in people with dementia and mild cognitive impairment. Alzheimer’s Dement. 2016;12(7):363. https://doi.org/10.1016/j.jalz.2016.06.673

Hu WT, McMillan C, Libon D, Leight S, Forman M, Lee VY, Grossman M. Multimodal predictors for Alzheimer disease in nonfluent primary progressive aphasia. Neurology. 2010;75(7):595-602. https://doi.org/10.1212/WNL.0b013e3181ed9c52

Liu P, Chen Z, Jones J, Huang D, Liu H. Auditory feedback control of vocal pitch during sustained vocalization: a cross-sectional study of adult aging. Plos One. 2011;7(6). https:// doi.org/10.1371/journal.pone.0022791

Ballard KJ, Savage S, Leyton CE, Vogel AP, Hornberger M, Hodges JR. Logopenic and nonfluent variants of primary progressive aphasia are differentiated by acoustic measures of speech production. PLoSOne. 2014;9(2).https://doi.org/10.1371/journal.pone.0089864

Menuet C, Cazals Y, Gestreau C, Borghgraef P, Gielis L, Dutschmann M, Hilaire G. Age-related impairment of ultrasonic vocalization in Tau. P301L mice: possible implication for progressive language disorders. Plos One. 2011;6(10). https://doi.org/10.1371/journal. pone.0025770

Romero T, Betancort M, Tari R, Dorta J. Estudio de parámetros acústicos y biomecánicos de voz asociado a enfermedades neurodegenerativas. Rev ORL. 2018;9(7):3-6. https:// doi.org/10.14201/orl.19357

Romero TR, Escudero FG, Redondo F, Montesinos MB. Acoustic analysis and speech therapy intervention in spoken and sung voice with the Lax Vox technique: on the subject of a case. Majorensis: Rev Electr Cienc Tecnol. 2018;(14):50-7.

Jarrold W, Peintner B, Wilkins D, Vergryi D, Richey C, Gorno ML, Ogar J. Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. Documento procedente del Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality; 2014. p. 27-37.

Horley K, Reid A, Burnham D. Emotional prosody perception and production in dementia of the Alzheimer’s type. J Speech Lang Hear Res. 2010;53(5):1132-46. https://doi. org/10.1044/1092-4388(2010/09-0030)

Mirheidari B, Blackburn D, Reuber M, Walker T, Christensen H. Diagnosing people with dementia using automatic conversation analysis. Interspeech 2016;1220-4. https://doi. org/10.21437/Interspeech.2016-857

Meilán JJ, Martínez F, Carro J, Sánchez JA, Pérez E. Acoustic markers associated with impairment in language processing in Alzheimer’s disease. Span J Psychol. 2012;15(2):487- 94. https://doi.org/10.5209/rev_SJOP.2012.v15.n2.38859

Al-Hameed S, Benaissa M, Christensen H. Simple and robust audio-based detection of biomarkers for Alzheimer’s disease. Documento procedente del 7th Workshop on Speech and Language Processing for Assistive Technologies SLPAT. 2016;32-36. https:// doi.org/10.21437/SLPAT.2016-6

Al-Hameed S, Benaissa M, Christensen H. Detecting and predicting Alzheimer’s disease severity in longitudinal acoustic data. Documento procedente de la International Conference on Bioinformatics Research and Applications. ACM. 2017;57-61. https://doi. org/10.1145/3175587.3175589

Al-Hameed S, Benaissa M, Christensen H, Mirheidari B, Blackburn D, Reuber M. A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints. PloS One. 2019;14(5). https://doi.org/10.1371/journal.pone.0217388

Rusz J, Cmejla R, Ruzickova H, Ruzicka E. Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease. J Acoust Soc Am. 2011;129(1):350-67. https://doi.org/10.1121/1.3514381

Gómez P, Rodellar V, Nieto V, Muñoz C, Mazaira LM, Martínez R, Fernández M. Characterizing neurological disease from voice quality biomechanical analysis. CognComputat. 2013;5(4):399-425. https://doi.org/10.1007/s12559-013-9207-2

Hernández JD, Gómez NM, Ruiz AJ, Arteaga LI. Análisis acústico de la voz: medidas temporales, espectrales y cepstrales en la voz normal con el Praat en una muestra de hablantes de español. Rev Investig Logopedia. 2017;7(2):108-27.

Ferrer CA, Bodt MD, Maryn Y, Heyning PVD, Hernández ME. Properties of the cepstral peak prominence and its usefulness in vocal quality measurements. Documento procedente del Fifth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications; 2007.

Fraser KC, Meltzer JA, Rudzicz F. Linguistic features identify Alzheimer’s disease in narrative speech. J Alzheimers Dis. 2016;49(2):407-22. https://doi.org/10.3233/jad-150520

Hinton G, Deng L, Yu D, Dahl G, Mohamed AR, Jaitly N, Sainath T. Deep neural networks for acoustic modeling in speech recognition. IEEE Sign Proc Mag. 2012;29. https://doi. org/10.1109/MSP.2012.2205597

Martínez F, Meilán JJ, Pérez E, Carro J, Arana JM. Patrones de prosodia expresiva en pacientes con enfermedad de Alzheimer. Psicothema. 2012;24(1):16-21.

Urrútia G. & Bonfill X. (2010). Declaración prisma: una propuesta para mejorar la publi- cación de revisiones sistemáticas y metaanálisis. Med Clin. 135(11):507-11. https://doi.org/10.1016/j.medcli.2010.01.015

Beltrami D, Calzà L, Gagliardi G, Ghidoni E, Marcello N, Favretti RR, Tamburini F. Automatic identification of mild cognitive impairment through the analysis of Italian spontaneous speech productions. Documento procedente del Tenth International Conference on Language Resources and Evaluation lrec. 2016;2086-93.

Gosztolya G, Vincze V, Tóth L, Pákáski M, Kálmán J, Hoffmann I. Identifying mild cognitive impairment and mild Alzheimer's disease based on spontaneous speech using ASR and linguistic features. Comp Speech Lang. 2019;53:181-97. https://doi.org/10.1016/j. csl.2018.07.007

Satt A, Hoory R, König A, Aalten P, Robert PH. Speech-based automatic and robust detection of very early dementia. Documento proceden de la Fifteenth Annual Conference of the International Speech Communication Association; 2014. https://doi. org/10.13140/2.1.1258.8805

Tóth L, Hoffmann I, Gosztolya G, Vincze V, Szatlóczki G, Bánréti Z, Kálmán J. A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech. Curr Alzheimer Res. 2018;15(2):130-8. https://doi.org/10.217 4/1567205014666171121114930

López-De-Ipina K, Solé J, Eguiraun H, Alonso JB, Travieso CM, Ezeiza A, Beitia B. Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: a fractal dimension approach. Comp Speech Lang. 2015;30(1):43-60. https://doi.org/10.1016/j. csl.2014.08.002

López-De-Ipina K, Martínez-De-Lizarduy U, Calvo PM, Mekyska J, Beitia B, Barroso N, Ecay M. Advances on automatic speech analysis for early detection of Alzheimer disease: a non-linear multi-task approach. Curr Alzheimer Res. 2018;15(2):139-48. https://doi.or g/10.2174/1567205014666171120143800

Martínez F, Meilán JJ, Carro J, Ivanova O. A prototype for the voice analysis diagnosis of Alzheimer’s disease. J Alzheimers Dis. 2018;64(2):473-81. https://doi.org/10.3233/JAD- 180037

Mirzaei S, El Yacoubi M, Garcia-Salicetti S, Boudy J, Senge Muvingi CK, Cristancho-Lacroix V, Kerhervé H, Rigaud Monnet A. Automatic speech analysis for early Alzheimer’s disease diagnosis [internet]. jetsan 2017: 6e Journées d’Etudes sur la Télésanté, Bourges, France; 2017. p. 114-6. Disponible en: https://www.archives-ouvertes.fr/hal-01618834/document

Barragán Pulido ML, Alonso Hernández JB, Ferrer Ballester MÁ, Travieso González CM, Mekyska J, Smékal Z. Alzheimer’s disease and automatic speech analysis: a review. Exp Syst Applicat. 2020;150:113213. https://doi.org/10.1016/j.eswa.2020.113213

Jarrold W, Peintner B, Wilkins D, Vergryi D, Richey C, Gorno ML & Ogar J. Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. Documento procedente del Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2014; 27-37.

Horley K, Reid A, Burnham D. Emotional prosody perception and production in dementia of the Alzheimer’s Type. J Speech Lang Hear Res. 2010;53(5):1132-46. https://doi. org/10.1044/1092-4388(2010/09-0030)

Boersma P, Weenink D. Praat, a system for doing phonetics by computer [internet]. [Citado 2019 abr 4]. Disponible en: http://www.fon.hum.uva.nl/praat/

De Jong NH, Wempe T. Praat script to detect syllable nuclei and measure speech rate automatically. Behav Res Methods. 2009;41(2):385-90. https://doi.org/10.3758/BRM.41.2.385

Sauder C, Bretl M, Eadie T. Predicting voice disorder status from smoothed measures of cepstral peak prominence using Praat and Analysis of Dysphonia in Speech and Voice (adsv). J Voice. 2017;31(5):557-66. https://doi.org/10.1016/j.jvoice.2017.01.006

Martínez F, Muela JA, Cortés P, Meilán JJ, Ferrándiz JA, Caparrós AE, Valverde IM. Can the acoustic analysis of expressive prosody discriminate schizophrenia? Span J Psychol. 2015;18. https://doi.org/10.1017/sjp.2015.85

Delgado J, León NM, Izquierdo LM, Llanos Y. Análisis cepstral de la voz normal y patológica en adultos españoles: medida de la prominencia del pico cepstral suavizado en vocales sostenidas versus habla conectada. Acta Otorrinolaringol Esp. 2018;69(3):134-40. https://doi.org/10.1016/j.otorri.2017.05.006

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