Vocal Tract Control in Parkinson's Disease

1981 ◽  
Vol 46 (4) ◽  
pp. 348-352 ◽  
Author(s):  
Jeri A. Logemann ◽  
Hilda B. Fisher
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Vocal Tract ◽  
Feature Analysis ◽  
High Fidelity ◽  
Phonetic Feature ◽  
Phonetic Transcription ◽  
Complete Closure ◽  
Articulatory Features ◽  
Production Characteristics

Consonant articulation patterns of 200 Parkinson patients were defined by two expert listeners from high fidelity tape recordings of the sentence version of the Fisher-Logemann Test of Articulation Competence (1971). Phonetic transcription and phonetic feature analysis were the methodologies used. Of the 200 patients, 90 (45%) exhibited some misarticulations. Phonetic data on these 90 dysarthric Parkinson patients revealed articulatory errors highly consistent in detailed production characteristics. Manner changes predominated. Phoneme classes that were most affected were the stop-plosives, affricates, and fricatives. In terms of perception features (Chomsky & Halle, 1968), the stop-plosives and affricates, which are normally [– continuant] were produced as [ + continuant] fricatives; fricatives that are [+ strident] were produced as [– strident]. There is no implication, however, that Parkinsonism involves a perception deficit. Analysis of the articulatory deficit reveals inadequate tongue elevation to achieve complete closure on stop-plosives and affricates, which can be expressed in production features as a change from [+ stop] to [+ fricative]. There was also inadequate close construction of the airway in lingual fricatives, which in articulatory features can be expressed as a change from [+ fricative] to [– fricative]. Both the incomplete contact for stops and the partial constriction for fricatives represent and inadequate narrowing of the vocal tract at the point of articulation. These results are discussed in relation to recent EMG studies and other physiologic examinations of Parkinsonian dysarthria.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

2021 ◽  
pp. 1-13
Author(s):  
Sen Liu ◽  
Han Yuan ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Real Life ◽  
Feature Analysis ◽  
Support Vector ◽  
Monitoring Methods ◽  
Resting Tremor ◽  
Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

Feedback and Feedforward Auditory-Motor Processes for Voice and Articulation in Parkinson's Disease

2021 ◽  
pp. 1-13
Author(s):  
Defne Abur ◽  
Austeja Subaciute ◽  
Ayoub Daliri ◽  
Rosemary A. Lester-Smith ◽  
Ashling A. Lupiani ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Control ◽  
Speech Intelligibility ◽  
Adaptive Responses ◽  
Formant Frequency ◽  
Motor Processes ◽  
Medication Schedule ◽  
Articulatory Features ◽  
Auditory Acuity

Purpose Unexpected and sustained manipulations of auditory feedback during speech production result in “reflexive” and “adaptive” responses, which can shed light on feedback and feedforward auditory-motor control processes, respectively. Persons with Parkinson's disease (PwPD) have shown aberrant reflexive and adaptive responses, but responses appear to differ for control of vocal and articulatory features. However, these responses have not been examined for both voice and articulation in the same speakers and with respect to auditory acuity and functional speech outcomes (speech intelligibility and naturalness). Method Here, 28 PwPD on their typical dopaminergic medication schedule and 28 age-, sex-, and hearing-matched controls completed tasks yielding reflexive and adaptive responses as well as auditory acuity for both vocal and articulatory features. Results No group differences were found for any measures of auditory-motor control, conflicting with prior findings in PwPD while off medication. Auditory-motor measures were also compared with listener ratings of speech function: first formant frequency acuity was related to speech intelligibility, whereas adaptive responses to vocal fundamental frequency manipulations were related to speech naturalness. Conclusions These results support that auditory-motor processes for both voice and articulatory features are intact for PwPD receiving medication. This work is also the first to suggest associations between measures of auditory-motor control and speech intelligibility and naturalness.

scholarly journals Force production characteristics in Parkinson's disease

1989 ◽  
Vol 76 (1) ◽  
Author(s):  
G.E. Stelmach ◽  
N. Teasdale ◽  
J. Phillips ◽  
C.J. Worringham
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Force Production ◽  
Production Characteristics

scholarly journals Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal

2021 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Vikas Mittal ◽  
R. K. Sharma
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Vocal Tract ◽  
Acoustic Parameter ◽  
Physical Parameters ◽  
Voice Signal ◽  
Analysis And Synthesis ◽  
The Central Nervous System ◽  
Pitch Ratio

The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.

scholarly journals Deep learning and automated Cell Painting reveal Parkinson’s disease-specific signatures in primary patient fibroblasts

2020 ◽  
Author(s):  
Lauren Schiff ◽  
Bianca Migliori ◽  
Ye Chen ◽  
Deidre Carter ◽  
Caitlyn Bonilla ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Specific Cell ◽  
High Fidelity ◽  
Healthy Controls ◽  
Cell Models ◽  
Disease Phenotypes ◽  
Primary Fibroblasts ◽  
Disease Specific

Drug discovery for Parkinson’s disease (PD) is impeded by the lack of screenable phenotypes in scalable cell models. Here we present a novel unbiased phenotypic profiling platform that combines automation, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 PD patients and carefully matched healthy controls, generating the largest publicly available Cell Painting dataset to date. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated unbiased deep embeddings from each image, and applied these to train machine learning models to detect morphological disease phenotypes. Interestingly, our models captured individual variation by identifying specific cell lines within the cohort with high fidelity, even across different batches and plate layouts, demonstrating platform robustness and sensitivity. Importantly, our models were able to confidently separate LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for PD modeling and drug screening applications.

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