ALZUMERICa decision support system for diagnosis and monitoring of cognitive impairment

  1. Unai Martinez de Lizarduy Sturtze 1
  2. Pilar Maria Calvo Salomon 1
  3. Pedro Gómez Vilda
  4. Mirian Ecay Torres 2
  5. Miren Karmele Lopez de Ipiña Peña 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

  2. 2 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

Zeitschrift:
Loquens : revista española de ciencias del habla

ISSN: 2386-2637

Datum der Publikation: 2017

Nummer: 4

Seiten: 3

Art: Artikel

DOI: 10.3989/LOQUENS.2017.037 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: Loquens : revista española de ciencias del habla

Zusammenfassung

Internet of things and smart cities are becoming a reality. Nowadays, more and more devices are interconnected and in order to deal with this new situation, data processing speeds are increasing to keep the pace. Smart devices like tablets and smartphones are accessible to a wide part of society in developed countries, and Internet connections for data exchange make it possible to handle large volumes of information in less time. This new reality has opened up the possibility of developing client-server architectures focused on clinical diagnosis in real time and at a very low cost. This paper illustrates the design and implementation of the ALZUMERIC system that is oriented to the diagnosis of Alzheimer’s disease (AD). It is a platform where the medical specialist can gather voice samples through non-invasive methods from patients with and without mild cognitive impairment (MCI), and the system automatically parameterizes the input signal to make a diagnose proposal. Although this type of impairment produces a cognitive loss, it is not severe enough to interfere with daily life. The present approach is based on the description of speech pathologies with regard to the following profiles: phonation, articulation, speech quality, analysis of the emotional response, language perception, and complex dynamics of the system. Privacy, confidentiality and information security have also been taken into consideration, as well as possible threats that the system could suffer, so this first prototype of services offered by ALZUMERIC has been targeted to a predetermined number of medical specialists.

Bibliographische Referenzen

  • Alzheimer's Disease International (2015). World Alzheimer Report 2015. Retrieved from https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf
  • Boersma, P., & Weenink, D. (2017). Praat: doing phonetics by computer [Computer program]. Retrieved from http://www.praat.org
  • CITA-Alzheimer Foundation, PGA Project (2017). Retrieved from http://www.cita-alzheimer.org/investigacion/proyectos
  • Dingemanse, M., Torreira, F., & Enfield, N. J. (2013). Is 'Huh?' a universal word? Conversational infrastructure and the convergent evolution of linguistic items. PLOS ONE, 8(11): e78273. https://doi.org/10.1371/journal.pone.0078273 PMid:24260108 PMCid:PMC3832628
  • Eibe, F., Mark, A., Hall, I., & Witten, H. (2016) (4th ed). The WEKA Workbench. Online Appendix for I. Witten, E. Frank, M. Hall, & C. Pal (Eds.), Data mining: Practical machine learning tools and techniques. New York: Elsevier, Morgan Kaufmann.
  • Faundez-Zanuy, M., Hussain, A., Mekyska, J., Sesa-Nogueras, E., Monte-Moreno, E., Esposito, A., … Lopez-de-Ipi-a, K. (2013). Biometric applications related to human beings: There is life beyond security. Cognitive Computation, 5(1), 136-151. https://doi.org/10.1007/s12559-012-9169-9
  • Gómez-Vilda P., Rodellar-Biarge, V., Nieto-Luis, V., López-de-Ipi-a, K., Álvarez-Marquina, A., Martínez-Olalla, R., … Martínez- Lage, P. (2015). Phonation biomechanic analysis of Alzheimer ?s Disease cases. Neurocomputing, 167, 83-93. https://doi.org/10.1016/j.neucom.2015.03.087
  • Klimova, B., Maresova, P., Valis, M., Hort J., & Kuca, K. (2015). Alzheimer's disease and language impairments: Social intervention and medical treatment. Clinical Interventions in Aging, 10, 1401–1408. PMid:26346123 PMCid:PMC4555976
  • Laske, C., Sohrabi, H. R., Frost, S. M., López-de-Ipi-a, K., Garrard, P., Buscema, M. … O'Bryant, S. E. (2015). Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimer & Dementia, 11(5), 561-578. https://doi.org/10.1016/j.jalz.2014.06.004 PMid:25443858
  • Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012) (5th ed.). Neuropsychological assessment. Oxford: Oxford University Press.
  • López-de-Ipiña K., Alonso, J.?B., Manuel Travieso, C., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M., … Martinez de Lizardui, U. (2013). On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors, 13, 6730–6745. https://doi.org/10.3390/s130506730 PMid:23698268 PMCid:PMC3690078
  • López-de-Ipiña, K., Martínez-de-Lizarduy, U., Barroso, N., Ecay-Torres, M., Martínez-Lage, P., Torres, F., & Faundez-Zanuy, M. (2015). Automatic analysis of Categorical Verbal Fluency for Mild Cognitive impartment detection: A non-linear language independent approach. (2015). 4th International Work Conference on Bioinspired Intelligence (IWOBI), 1, 101-104. https://doi.org/10.1109/IWOBI.2015.7160151
  • López-de-Ipiña, K., Martinez de Lizarduy, U., Calvo, P., Beita, B., García-Melero, J., Ecay-Torres, M., … Faundez-Zanuy, M. (2017). Analysis of disfluencies for automatic detection of Mild Cognitive Impairment: a deep learning approach. 4th International Work Conference on Bioinspired Intelligence (IWOBI), 1, 1-4.
  • López-de-Ipiña K., Satue-Villar, A., Faundez-Zanuy, M., Arreola, V., Ortega, O., Clavé, P. … Calvo, P. (2016). Advances in a multimodal approach for dysphagia analysis based on automatic voice analysis. In S. Bassis, A. Esposito, F. Morabito, & E. Pasero (Eds.), Advances in Neural Networks. Smart Innovation, Systems and Technologies, Vol. 54 (pp. 201-211). Cham: Springer.
  • MATLAB (2017). www.mathworks.com
  • Mekyska, J., Janousova, E., Gomez-Vilda, P., Smekal, Z., Rektorova, I., Eliasova, I., … López-de-Ipi-a, K. (2015). Robust and complex approach of pathological speech signal analysis. Neurocomputing, 167, 94-111. https://doi.org/10.1016/j.neucom.2015.02.085
  • Picard, R. R., & Cook, D. (1984). Cross-validation of regression models. Journal of the American Statistical Association, 79 (387), 575–583. https://doi.org/10.1080/01621459.1984.10478083
  • Ruff, R. M., Light, R. H., Parker, S. B., & Levin, H. S. (1997). The psychological construct of word fluency. Brain and Language, 57, 394-405. https://doi.org/10.1006/brln.1997.1755 PMid:9126423
  • Solé-Casals, J., & Zaiats, V. (2010). A non-linear VAD for noisy environments. Cognitive Computation, 2(3), 191-198. https://doi.org/10.1007/s12559-010-9037-4
  • WEKA (2017). Retrieved from http://www.cs.waikato.ac.nz/ml/weka
Fuente de los datos: Dialnet