An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson’s patients

Karan, Biswajit; Sahu, Sitanshu Sekhar; Orozco-Arroyave, Juan Rafael

doi:10.1016/j.bbe.2022.04.003

Cited by 12 publications

(5 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the same time, the index shows a pattern of evolutionary and incremental behavior in the course of PD. These findings are coherent with a previous work [43][44][45] , allowing to argue that the deterioration of speech performance can appear even when the disease has just been diagnosed, being progressive as the stage of the disease progresses, which is explained by the presence of axial symptoms and bradykinesia affecting the labial, mandibular and lingual musculature in people diagnosed with PD. On the contrary, the fact that the DDKcvp% index can detect articulatory deficits from stage 1 of PD would give it a key role -as a diagnostic complement-to perceptual assessments that require the expertise of the evaluator.…”

Section: Discussionsupporting

confidence: 91%

Use of the diadochokinetic index DDKCCVP% in the detection of articulatory inaccuracies in Parkinson’s disease: a preliminary exploratory study

Sanhueza-Garrido,

Rojas-Zepeda,

García-Flores

2023

Rev. CEFAC

View full text Add to dashboard Cite

Purpose: to explore whether the diadochokinetic index of syllable production variability (DDKcvp%) is useful for the detection of articulatory inaccuracies in different stages of Parkinson's disease (PD). Methods: a diadochokinetic speech task of the consonant-vowel type [pa-pa-pa] was applied to 18 people with PD (classified in stages of progression from 1 to 5 according to the Hoehn & Yahr scale) to determine their diadochokinetic performance. Records of the study group were compared to a reference value obtained from a control group made up of 40 subjects. Results: 77.78% of the study group showed articulatory inaccuracies. The DDKcvp% index allowed to detect articulatory difficulties from stage 1 of PD, and its variation showed that articulatory deficits were permanent from stage 2 onwards. A significant and direct correlation was obtained between the increase in the DDKcvp% index and the increase in the stage of evolution of the participants. Conclusion: DDKcvp% index evaluated by means of a simple and fast consonant-vowel diadochokinetic task [pa-pa-pa], could be useful to determine articulatory inaccuracies in different stages of PD, although it is suggested that investigation of this index in larger sample sizes be conducted.

show abstract

Section: Discussionsupporting

confidence: 91%

Use of the diadochokinetic index DDKCCVP% in the detection of articulatory inaccuracies in Parkinson’s disease: a preliminary exploratory study

Sanhueza-Garrido,

Rojas-Zepeda,

García-Flores

2023

Rev. CEFAC

View full text Add to dashboard Cite

show abstract

“…The aim was to support the clinicians in identifying subtle acoustic alterations that may be difficult to detect perceptually, in order to allow an early diagnosis of dysarthria, even when clinical symptoms are subclinical. Furthermore, the relationships between PD and dysarthria are not bidirectional: not all Parkinson’s patients necessarily develop dysarthria, and the presence and severity of dysarthria can vary from one patient to another and evolve at a different pace than the progression of the disease ( Dias et al, 2016 ; Karan et al, 2022 ). Moreover, the analysis of demographic data highlights a lack of correlation between the progression of the disease (disease stages and duration since diagnosis) and the presence or absence of dysarthric symptoms.…”

Section: Discussionmentioning

confidence: 99%

Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson’s disease

Roland,

Huet,

Harmegnies

et al. 2023

Front. Psychol.

View full text Add to dashboard Cite

ObjectivesOur aim is to detect early, subclinical speech biomarkers of dysarthria in Parkinson’s disease (PD), i.e., systematic atypicalities in speech that remain subtle, are not easily detectible by the clinician, so that the patient is labeled “non-dysarthric.” Based on promising exploratory work, we examine here whether vowel articulation, as assessed by three acoustic metrics, can be used as early indicator of speech difficulties associated with Parkinson’s disease.Study designThis is a prospective case–control study.MethodsSixty-three individuals with PD and 35 without PD (healthy controls-HC) participated in this study. Out of 63 PD patients, 43 had been diagnosed with dysarthria (DPD) and 20 had not (NDPD). Sustained vowels were recorded for each speaker and formant frequencies were measured. The analyses focus on three acoustic metrics: individual vowel triangle areas (tVSA), vowel articulation index (VAI) and the Phi index.ResultstVSA were found to be significantly smaller for DPD speakers than for HC. The VAI showed significant differences between these two groups, indicating greater centralization and lower vowel contrasts in the DPD speakers with dysarhtria. In addition, DPD and NDPD speakers had lower Phi values, indicating a lower organization of their vowel system compared to the HC. Results also showed that the VAI index was the most efficient to distinguish between DPD and NDPD whereas the Phi index was the best acoustic metric to discriminate NDPD and HC.ConclusionThis acoustic study identified potential subclinical vowel-related speech biomarkers of dysarthria in speakers with Parkinson’s disease who have not been diagnosed with dysarthria.

show abstract

“…3) Speech Dysarthria Recognition: In the earliest stages of PD, 90% of patients experience voice abnormalities, specifically hypokinetic dysarthria [97]. Researchers in [39] examined the problem of speech-based categorization of amyotrophic lateral sclerosis (ALS) patients, Parkinson's disease patients, and healthy controls (HC).…”

Section: B DL For Non-conventional Pd-related Diagnostic Targetsmentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-theart performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.

show abstract

An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson’s patients

Cited by 12 publications

References 57 publications

Use of the diadochokinetic index DDKCCVP% in the detection of articulatory inaccuracies in Parkinson’s disease: a preliminary exploratory study

Use of the diadochokinetic index DDKCCVP% in the detection of articulatory inaccuracies in Parkinson’s disease: a preliminary exploratory study

Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson’s disease

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Contact Info

Product

Resources

About