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Evaluation of Acoustic Features for Early Diagnosis of Alzheimer Disease

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Intelligent Systems Design and Applications (ISDA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1181))

Abstract

Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilities of affected peoples. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The purpose of this article is extracting a set of acoustic features from Dementia Bank conversations from subjects with and without Alzheimer’s disease. Extracted features will be trained with Machine Learning (ML) algorithms to testing the detection accuracy.

Obtained results indicate that the proposed features extracted from the speech samples can be used to distinguish between the patients with Alzheimer’s disease and the healthy control group. Classification accuracy over 90% was obtained with Support Vector Machine (SVM) classifier.

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References

  1. Alzheimer, A.: eine eigenartige Erkrankung der Hirnrinde. Allgemeine Zeitschrift fűr Psychiatrie 64, 146–148 (1907)

    Google Scholar 

  2. Petersen, R.: Mild cognitive impairment: aging to Alzheimer’s disease. In: Petersen, R. (ed.) Clinical Features, pp. 15–40. Oxford University Press, New York (2003)

    Google Scholar 

  3. Gaugler, J., James, B., Johnson, T., Scholz, K., Weuve, J., Alzheimer’s-Association.: 2016 Alzheimer’s Disease Facts and Figures. Alzheimer’s Dementia 12, 459–509 (2016)

    Google Scholar 

  4. Ankri, J.: Prévalence incidence et facteurs de risque de la maladie d’Alzheimer. Gérontologie et société 128/129, 129–141 (2009)

    Google Scholar 

  5. Thomas, P., Clement, J.P., Hazif-Thomas, C., Leger, J.M.: Family, Alzheimer’s disease and negative symptoms. Int. J. Geriatric Psychiatry 16, 192–202 (2001)

    Article  Google Scholar 

  6. Bucks, R.S., Singh, S., Cuerden, J.M., Wilcock, G.K.: Analysis of spontaneous, conversational speech in dementia of Alzheimer type evaluation of an objective technique for analysing lexical performance. Aphasiology 14(1), 71–91 (2000)

    Article  Google Scholar 

  7. Cardebat, D., Aithamon, B., Puel, M.: Les troubles du langage dans les démences de type Alzheimer. In: Eustache, F., Agniel, A. (eds.) Neuropsychologie cliniques des démences: Evaluation et prises en charge, pp. 213–223. Marseille: Solal (1995)

    Google Scholar 

  8. Rosser, A., Hodges, J.R.: Initial letters and semantic category fluency in Alzheimer’s disease, Huntington’s disease, and progressive supranuclear palsy. J. Neurol. Neurosurg. Psychiatry 57, 1389–1394 (1994)

    Article  Google Scholar 

  9. Mickes, L., Wixted, J.T., Fennema-Notestine, C., Galasko, D., Bondi, M.W., Thal, L.J.: Progressive impairment on neuropsychological tasks in a longitudinal study of preclinical Alzheimer’s disease. Neuropsychology 21(6), 696–705 (2007)

    Google Scholar 

  10. Ben Ammar, R., Ben Ayed, Y.: Speech processing for early Alzheimer disease diagnosis: machine learning based approach. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), Aqaba, pp. 1–8 (2018)

    Google Scholar 

  11. McNamara, P., Obler, L.K., Au, R., Durso, R., Albert, M.L.: Speech monitoring skills in Alzheimer’s disease, Partkinson’s disease, and normal aging. Brain Lang. 42, 38–51 (1992)

    Google Scholar 

  12. Barkat-Defradas, M., Martin, S., Rico-Duarte, L., Brouillet, D.: Les troubles de la parole dans la maladie d’Alzheimer. 27èmes journées d’études sur la Parole. Avignon, France (2008)

    Google Scholar 

  13. Cummings, J.L., Benson, D.F., Hill, M., Read, S.: Aphasia in dementia of the Alzheimer type. Neurology 35, 394–397 (1985)

    Article  Google Scholar 

  14. Selnes, F.O.A., Carson, K., Rovner, B., Gordon, M.D.: Language dysfunction in early- and late-onset possible Alzheimer’s disease. Neurology 38, 1053–1056 (1988)

    Google Scholar 

  15. Loebel, J.P., Dager, S.R., Berg, G., Hyde, T.: Fluency of speech and selfawareness of memory deficits in Alzheimer’s disease. Int. J. Geriatric Psychiatry 5, 41–45 (1990)

    Article  Google Scholar 

  16. Wingate, M.E.: Fluency and disfluency; illusion and identification. J. Fluency Disorders 12, 79–101 (1987)

    Article  Google Scholar 

  17. López-de-Ipiña, K., Alonso Travieso, J.-B., Solé-Casals, C.-M., Egiraun, J., Faundez-Zanuy, H., Ezeiza, M., Barroso, A., Ecay-Torres, N., Martinez-Lage, M., Lizardui, P.: On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors 13, 6730–6745 (2013)

    Article  Google Scholar 

  18. Hoffmann, I., Németh, D., Dye, C., Pákáski, M., Irinyi, T., Kálmán, J.: Temporal features of spontaneous speech in Alzheimer’s disease. Int. J. Speech Lang. Pathol. 12, 29–34 (2010). https://doi.org/10.3109/17549500903137256

    Article  Google Scholar 

  19. Jarrold, W., Peintner, B., Wilkins, D., Vergryi, D., Richey, C., Gorno-Tempini, M.L., et al.: Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. In: Proceedings of CLPsych (Baltimore, MD), pp. 27–37 (2014)

    Google Scholar 

  20. Satt, A., Hoory, R., König, A., Aalten, P., Robert, P.H.: Speech-based automatic and robust detection of very early dementia. In: Interspeech (Singapore) (2014)

    Google Scholar 

  21. Meilan, J., Martnez-Snchez, F., Carro, J., Lopez, D., Millian- Morell, J., Arana, J.: Speech in Alzheimer’s disease: can temporal and acoustic parameters discriminate dementia? Dementia Geriatric Cogn. Disorders 37(5–6), 327–334 (2014)

    Article  Google Scholar 

  22. López-de-Ipiña, K., Martinez-de-Lizarduy, U., Calvo, P. M., Beitia, B., García-Melero, J., Fernández, E., Sanz, P. On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment. Neural Comput. Appl. 1–9 (2018)

    Google Scholar 

  23. König, A., Satt, A., Sorin, A., Hoory, R., Toledo-Ronen, O., Derreumaux, A., Manera, V., Verhey, F., Aalten, P., Robert, P.H., David, R.: Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimer’s Dementia: Diagnosis Assess. Dis. Monit. 1(1), 112–124 (2015)

    Google Scholar 

  24. Tóth, L., Gosztolya, G., Vincze, V., Hoffmann, I., Szatlóczki, G., Biró, E., Zsura, F., Pákáski, M., Kálmán, J.: Automatic detection of mild cognitive impairment from spontaneous speech using ASR. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)

    Google Scholar 

  25. Becker, J.T., Boiler, F., Lopez, O.L., Saxton, J., McGonigle, K.L.: The natural history of Alzheimer’s disease: Description of study cohort and accuracy of diagnosis. Arch. Neurol. 51, 585–594 (1994)

    Article  Google Scholar 

  26. Praat: doing Phonetics by Computer. www.fon.hum.uva.nl/praat

  27. Zwetsch, I., Fagundes, R., Russomano, T., Scolari, D.: Digital signal processing in the differential diagnosis of beningn larynx diseases, Porto Alegre (2006)

    Google Scholar 

  28. Al-Nasheri, A., Muhammad, G., Alsulaiman, M., Ali, Z., Malki, K.H., Mesallam, T.A., Ibrahim, M.F.: Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions. IEEE Access 6, 6961–6974 (2017)

    Article  Google Scholar 

  29. Pravena, D., Dhivya, S., Durga Devi, A.: Pathological voice recognition for vocal fold disease. Int. J. Comput. Appl. 47(13) (2012)

    Google Scholar 

  30. Vikram, C.M., Umarani, K.: Pathological voice analysis to detect neurological disorders using MFCC & SVM. Int. J. Adv. Electr. Electron. Eng. 2(4) (2013)

    Google Scholar 

  31. Murphy, P., Akande, O.: Cepstrum-based estimation of the harmonics-tonoise ratio for synthesized and human voice signals. In: Nonlinear Analyses and Algorithms for Speech Processing. Barcelona. LNAI, vol. 3817. Springer, Heidelberg (2005)

    Google Scholar 

  32. Balamurugan, M., Nancy, A., Vijaykumar, S.: Alzheimer’s disease diagnosis by using dimensionality reduction based on Knn classifier. Biomed. Pharmacol. J. 10(4) (2017)

    Google Scholar 

  33. Corte, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Multimedia Information System and Advanced Computing Laboratory, Sfax University, Sfax, Tunisia

    Randa Ben Ammar & Yassine Ben Ayed

Authors
  1. Randa Ben Ammar
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  2. Yassine Ben Ayed
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Corresponding author

Correspondence to Randa Ben Ammar .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Université Paris-Est Créteil Val-de-Ma, Creteil Cedex, France

    Patrick Siarry

  3. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

  4. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

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Ammar, R.B., Ayed, Y.B. (2021). Evaluation of Acoustic Features for Early Diagnosis of Alzheimer Disease. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_17

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