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
Camilo Rodriguez Fandiño
