The Effects of Symptom Onset Location on Automatic Amyotrophic Lateral Sclerosis Detection Using the Correlation Structure of Articulatory Movements

Autor/inn/en	Wisler, Alan; Teplansky, Kristin; Heitzman, Daragh; Wang, Jun
Titel	The Effects of Symptom Onset Location on Automatic Amyotrophic Lateral Sclerosis Detection Using the Correlation Structure of Articulatory Movements
Quelle	In: Journal of Speech, Language, and Hearing Research, 64 (2021) 6, S.2276-2286 (11 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wisler, Alan) ORCID (Wang, Jun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Symptoms (Individual Disorders); Neurological Impairments; Articulation (Speech); Brain Hemisphere Functions; Severity (of Disability); Speech Skills; Speech Impairments; Articulation Impairments; Psychomotor Skills; Models; Predictor Variables; Artificial Intelligence; Texas + Suchen Sie Ihr Suchwort? Psychiatrische Symptomatik; Neurodegenerative Erkrankung; Schweregrad; Mündliche Leistung; Sprachfertigkeit; Speech impairment; Speech handicap; Speech handicaps; Language handicps; Language impairments; Sprachbehinderung; Artikulationsstörung; Psychomotorische Aktivität; Analogiemodell; Prädiktor; Künstliche Intelligenz
Abstract	Purpose: Kinematic measurements of speech have demonstrated some success in automatic detection of early symptoms of amyotrophic lateral sclerosis (ALS). In this study, we examined how the region of symptom onset (bulbar vs. spinal) affects the ability of data-driven models to detect ALS. Method: We used a correlation structure of articulatory movements combined with a machine learning model (i.e., artificial neural network) to detect differences between people with ALS and healthy controls. The performance of this system was evaluated separately for participants with bulbar onset and spinal onset to examine how region of onset affects classification performance. We then performed a regression analysis to examine how different severity measures and region of onset affects model performance. Results: The proposed model was significantly more accurate in classifying the bulbar-onset participants, achieving an area under the curve of 0.809 relative to the 0.674 achieved for spinal-onset participants. The regression analysis, however, found that differences in classifier performance across participants were better explained by their speech performance (intelligible speaking rate), and no significant differences were observed based on region of onset when intelligible speaking rate was accounted for. Conclusions: Although we found a significant difference in the model's ability to detect ALS depending on the region of onset, this disparity can be primarily explained by observable differences in speech motor symptoms. Thus, when the severity of speech symptoms (e.g., intelligible speaking rate) was accounted for, symptom onset location did not affect the proposed computational model's ability to detect ALS. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01