Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis.
Purpose: Oral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 65; no. 3; pp. 940 - 954 |
|---|---|
| Autores principales: | , , , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
American Speech-Language-Hearing Association
Mar2022
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=155635788&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 155635788 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Mar2022 vid: 65 iid: 3 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 155635788 10.1044/2021_JSLHR-21-00503 ppf: 940 ppct: 14 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis. aug: au: Tanchip, Chelsea Guarin, Diego L. McKinlay, Scotia Barnett, Carolina Kalra, Sanjay Genge, Angela Korngut, Lawrence Green, Jordan R. Berry, James Zinman, Lorne Yadollahi, Azadeh Abrahao, Agessandro Yunusova, Yana affil: Department of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada. Department of Biomedical Engineering, Florida Institute of Technology, Melbourne. Division of Neurology, Department of Medicine, University of Toronto and University Health Network, Ontario, Canada. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada. Division of Neurology, University of Alberta, Edmonton, Canada. Clinical Research Unit, Montreal Neurological Institute & Hospital, and Department of Neurology and Neurosurgery, McGill University, Québec, Canada. Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Alberta, Canada. Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA. Department of Neurology, Massachusetts General Hospital, Boston. Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada. Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada. KITE, Toronto Rehabilitation Institute, University Health Network, Ontario, Canada mInstitute of Biomedical Engineering, University of Toronto, Ontario, Canada. su: Task performance Dysarthria Severity of illness index Speech perception Intelligibility of speech Impedance audiometry Amyotrophic lateral sclerosis Automation sug: subj: Task performance Dysarthria Severity of illness index Speech perception Intelligibility of speech Impedance audiometry Amyotrophic lateral sclerosis Automation ab: Purpose: Oral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms have been predominantly validated in individuals with no perceptible to mild dysarthria. The behavior of these algorithms across dysarthria severity is largely unknown. Likewise, these algorithms have not been tested equally for various syllable types. The goal of this study was to evaluate the performance of five common DDK algorithms as a function of dysarthria severity, considering syllable types. Method: We analyzed 282 DDK recordings of /ba/, /pa/, and /ta/ from 145 participants with amyotrophic lateral sclerosis. Recordings were stratified into mild, moderate, or severe dysarthria groups based on individual performance on the Speech Intelligibility Test. Analysis included manual and automatic estimation of the number of syllables, DDK rate, and cycle-to-cycle temporal variability (cTV). Validation metrics included Bland–Altman mixed-effects limits of agreement between manual and automatic syllable counts, recall and precision between manual and automatic syllable boundary detection, and Kendall’s tau-b correlations between manual and algorithm-detected DDK rate and cTV. Results: The amplitude-based algorithm (absolute energy) yielded the strongest correlations with manual analysis across all severity groups for DDK rate (τ = 0.7–0.84) and cTV (τ = 0.7–0.84) and the narrowest limits of agreement (−5.92 to 7.12 syllable difference). Moreover, this algorithm also provided the highest mean recall and precision across severity groups for /ba/ and /pa/, but with significantly more variation for/ta/. Conclusions: Algorithms based on signal energy analysis appeared to be the most robust for DDK analysis across dysarthria severity and syllable types; however, it remains prone to error against severe dysarthria and alveolar syllable context. Further development is needed to address this important issue. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|