Abstract
Parkinson’s disease (PD) is a neuro-degenerative disorder that produces motor and non-motor impairments. Non-motor impairments include communication and mood disorders. Most of the studies in the literature have been focused on the analysis of motor symptoms. However, non-motor signs are also present in most of the cases. This paper addresses the study of language production as a potential tool to diagnose and monitor the neurological state of PD patients. The study proposes the use of natural language processing methods to extract features from transcriptions obtained from spontaneous speech recordings to discriminate between healthy control people and PD patients. The analysis considered classical features such as Bag of Words and Term Frequency-Inverse Document Frequency, along with methods based on word-embeddings. Accuracies of up to 72% are obtained when discriminating between PD patients and healthy subjects, which confirms that there is information embedded in the language production that can be used for the assessment of the disease.
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References
Hornykiewicz, O.: Biochemical aspects of Parkinson’s disease. Neurology 51(2 Suppl 2), S2–S9 (1998)
Smith, K.M., Caplan, D.N.: Communication impairment in Parkinson’s disease: impact of motor and cognitive symptoms on speech and language. Brain Lang. 185, 38–46 (2018)
Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)
Murray, L.L., Lenz, L.P.: Productive syntax abilities in Huntington’s and Parkinson’s diseases. Brain Cogn. 46(1–2), 213–219 (2001)
Vanhoutte, S., De Letter, M., Corthals, P., et al.: Quantitative analysis of language production in Parkinson’s disease using a cued sentence generation task. Clin. linguist. Phonetics 26(10), 863–881 (2012)
García, A.M., Carrillo, F., Orozco-Arroyave, J.R., Trujillo, N., et al.: How language flows when movements don’t: an automated analysis of spontaneous discourse in Parkinson’s disease. Brain Lang. 162, 19–28 (2016)
Fraser, K.C., Meltzer, J.A., Rudzicz, F.: Linguistic features identify Alzheimer’s disease in narrative speech. J. Alzheimers Dis. 49(2), 407–422 (2016)
Wankerl, S., Nöth, E., Evert, S.: An N-gram based approach to the automatic diagnosis of Alzheimer’s disease from spoken language. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (2017)
Klumpp, P., Fritsch, J., Noeth, E.: ANN-based Alzheimer’s disease classification from bag of words. In: 13th ITG-Symposium on Speech Communication, pp. 1–4. VDE (2018)
Becker, J.T., et al.: The natural history of Alzheimer’s disease: description of study cohort and accuracy of diagnosis. Arch. Neurol. 51(6), 585–594 (1994)
Goodglass, H., Kaplan, E.: Boston Naming Test: Scoring Booklet. Lea & Febiger, Philadelphia (1983)
Orozco-Arroyave, J.R., Arias-Londoño, J.D., Vargas-Bonilla, J.F., et al.: New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease. In: LREC, pp. 342–347 (2014)
Bird, S., Loper, E.: NLTK: the natural language toolkit. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, pp. 31. Association for Computational Linguistics (2004)
Reese, S., Boleda, G., Cuadros, M., et al.: Wikicorpus: a word-sense disambiguated multilingual wikipedia corpus (2010)
Vásquez-Correa, J.C., Orozco-Arroyave, J.R., Nöth, E.: Convolutional neural network to model articulation impairments in patients with Parkinson’s disease. In: Proceedings of INTERSPEECH, pp. 314–318 (2017)
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Pérez-Toro, P.A., Vásquez-Correa, J.C., Strauss, M., Orozco-Arroyave, J.R., Nöth, E. (2019). Natural Language Analysis to Detect Parkinson’s Disease. In: Ekštein, K. (eds) Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science(), vol 11697. Springer, Cham. https://doi.org/10.1007/978-3-030-27947-9_7
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DOI: https://doi.org/10.1007/978-3-030-27947-9_7
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