Detection of Subclinical Mild Traumatic Brain Injury (mTBI) Through Speech and Gait

Talkar, Tanya; Yuditskaya, Sophia; Williamson, James R.; Lammert, Adam C.; Rao, Hrishikesh M.; Hannon, Daniel; O’Brien, Anne; Vergara‐Diaz, Gloria; DeLaura, Richard; Sturim, Douglas E.; Ciccarelli, Gregory; Zafonte, Ross; Palmer, Jeff; Bonato, Paolo; Quatieri, Thomas F.

doi:10.21437/interspeech.2020-2651

Cited by 16 publications

(21 citation statements)

References 9 publications

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“…Novel signal processing techniques were used to measure movement dispersion during gait periods and movement dimensionality during non-gait low-amplitude movement periods. The accelerometry analysis approaches (movement dispersion and dimensionality) have been validated previously on torso-worn accelerometry data [ 20 , 25 , 43 ]. The movement dimensionality approach has also been applied to many alternative modalities, including the analysis of speech differences due to PD [ 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…A general approach was developed that quantifies movement dimensionality in this way, using correlation patterns among multichannel feature sets. This approach has been used in many studies to detect and estimate alterations in neuromotor coordination from speech [ 23 , 24 , 25 , 26 ], including motor and cognitive symptoms due to Parkinson’s disease [ 21 , 22 ]. The approach has also been used for detecting alterations in torso accelerations during gait due to load carriage [ 20 ] and mild traumatic brain injury [ 25 ], as well as for detecting alterations in hand movements during drawing due to autism [ 26 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The eigenvalues of the correlation matrix,

, rank ordered from largest to smallest, quantify the correlation structure. Consistent with past practice when using correlation structure features [ 23 , 24 , 25 ], the eigenspectra feature vectors were concatenated across the four delay scales. Table 2 summarizes the parameters used to extract these features, which is the same parameterization typically used in the literature [ 23 , 24 , 25 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Table 3 summarizes the GMM parameters, which have standard values that have been used in multiple previous studies [ 24 , 25 , 26 ]. The GMM output when presented with a feature vector from the test data is a likelihood score for each class model.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…This is an important distinction from other analyses of accelerometry, which are designed to detect particular features such as tremor, bradykinesia, and gait freezing [ 18 ]. In a novel approach for PD detection, we investigate the use of correlation-structure analysis techniques that were originally developed for detecting changes in EEG functional connectivity to predict epileptic seizures [ 19 ] and that have since been shown to be advantageous for several biomedical applications [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

Williamson

Telfer

Mullany

et al. 2021

Sensors

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Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

“…The eigenvalues of the correlation matrix,

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

Williamson

Telfer

Mullany

et al. 2021

Sensors

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Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns

Talkar

Low

Simpkin

et al. 2023

Sci Rep

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In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

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Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

Kent

Kim

Chen

2022

J Speech Lang Hear Res

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Purpose: The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis. Method: Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate. Results: Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions. Discussion: A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.

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Detection of Subclinical Mild Traumatic Brain Injury (mTBI) Through Speech and Gait

Cited by 16 publications

References 9 publications

Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns

Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

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