Objective Assessment of Vocal Tremor

Objective: To determine the potential for improving amyotrophic lateral sclerosis (ALS) clinical trials by having patients or caregivers perform frequent selfassessments at home. Methods and Participants: We enrolled ALS patients into a nonblinded, longitudinal 9-month study in which patients and caregivers obtained daily data using several different instruments, including a slow-vital capacity device, a hand grip dynamometer, an electrical impedance myographybased fitness device, an activity tracker, a speech app, and the ALS functional rating scale-revised. Questions as to acceptability were asked at two time points. Results: A total of 113 individuals enrolled, with 61 (43 men, 18 women, mean age 60.1 AE 9.9 years) collecting a minimum of 7 days data and being included in the analysis. Daily measurements resulted in more accurate assessments of the slope of progression of the disease, resulting in smaller sample size estimates for a hypothetical clinical trial. For example, by performing daily slow-vital capacity measurements, calculated sample size was reduced to 182 subjects/ study arm from 882/arm for monthly measurements. Similarly, performing the ALS functional rating scale weekly rather than monthly led to a calculated sample size of 73/arm as compared to 274/arm. Participants generally found the procedures acceptable and, for many, improved their sense of control of their disease. Interpretation: Frequent at-home measurements using standard tools holds the prospect of tracking progression and reducing sample size requirements for clinical trials in ALS while also being acceptable to the patients. Future studies in this and other neurological disorders should consider adopting this approach to data collection.

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“…The following data collection tools and methods were used in this study: 7 These results will be reported separately. 5 Activity tracker.…”

Section: Measurement Tools and Methodsmentioning

confidence: 99%

“… Speech analysis . A separate ALS AT HOME speech app and database was developed by Aural Analytics (Phoenix, Arizona, USA) 7 . These results will be reported separately. Activity tracker .…”

Section: Methodsmentioning

confidence: 99%

Improved ALS clinical trials through frequent at‐home self‐assessment: a proof of concept study

Rutkove¹,

Narayanaswami²,

Berisha

et al. 2020

Ann Clin Transl Neurol

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“…Tremor features displayed the strongest effect size when comparing preHD and Controls. But the unequal level of data loss in each group (51% HD, 38% preHD, and 25% Controls) suggests defining estimation methods that incorporate tremor instability and better tremor tracking methods [14] for future work.…”

Section: Resultsmentioning

confidence: 99%

“…Previous studies have used sustained phonation for the assessment of various neurodegenerative disorders such as Parkinson's disease [13], Amyotrophic Lateral Sclerosis [14] and also Huntington's Disease [10]. These studies have extracted a number of hand-crafted features from the sustained phonation and built discriminative models to distinguish patients from controls.…”

Section: Related Workmentioning

confidence: 99%

Vocal Markers from Sustained Phonation in Huntington’s Disease

Riad¹,

Titeux²,

Montillot³

et al. 2020

Interspeech 2020

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Disease-modifying treatments are currently assessed in neurodegenerative diseases. Huntington's Disease represents a unique opportunity to design automatic sub-clinical markers, even in premanifest gene carriers. We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up. We used two sets of features: Phonatory features and Modulation Power Spectrum Features. We found that phonation is not sufficient for the identification of sub-clinical disorders of premanifest gene carriers. According to our regression results, Phonatory features are suitable for the predictions of clinical performance in Huntington's Disease.

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“…We show that this representation correlates strongly with clinical perception of hypernasality across several different neurological disorders, even when trained on one disorder and evaluated on a different disorder. Such assessment has a potential advantage as an inexpensive, non-invasive and simple-to-administer method, scalable to large and diverse populations [57] [58].…”

Section: B Contributionsmentioning

confidence: 99%

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features

Saxon,

Tripathi,

Jiao

et al. 2019

Preprint

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Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained.Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g. training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.

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Objective Assessment of Vocal Tremor

Cited by 6 publications

References 24 publications

Improved ALS clinical trials through frequent at‐home self‐assessment: a proof of concept study

Improved ALS clinical trials through frequent at‐home self‐assessment: a proof of concept study

Vocal Markers from Sustained Phonation in Huntington’s Disease

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features

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