Fluency Changes due to Sports-Related Concussion

Patel, Sona; Grabowski, Caryn; Dayalu, Vikram N.; Cunningham, Mercedes; Testa, Anthony J.

doi:10.1101/2021.09.19.21263791

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The authors would like to thank Ji Sook Ahn and the athletics trainers Caroline Brennan, Deja Craig-Lucien, Mercedes Cunningham, Catherine Lass, Kaitlin Kelly, Gabriel Scher, and Nicholas Schulman for their collective help and support in coordinating student athlete testing, and the student athletes for their time and efforts in participation in this study. A preprint of this article was previously made available ( Patel et al, 2021 ).…”

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confidence: 99%

Speech error rates after a sports-related concussion

Patel

Grabowski²,

Dayalu³

et al. 2023

Front. Psychol.

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BackgroundAlterations in speech have long been identified as indicators of various neurologic conditions including traumatic brain injury, neurodegenerative diseases, and stroke. The extent to which speech errors occur in milder brain injuries, such as sports-related concussions, is unknown. The present study examined speech error rates in student athletes after a sports-related concussion compared to pre-injury speech performance in order to determine the presence and relevant characteristics of changes in speech production in this less easily detected neurologic condition.MethodsA within-subjects pre/post-injury design was used. A total of 359 Division I student athletes participated in pre-season baseline speech testing. Of these, 27 athletes (18–22 years) who sustained a concussion also participated in speech testing in the days immediately following diagnosis of concussion. Picture description tasks were utilized to prompt connected speech samples. These samples were recorded and then transcribed for identification of errors and disfluencies. These were coded by two trained raters using a 6-category system that included 14 types of error metrics.ResultsRepeated measures analysis of variance was used to compare the difference in error rates at baseline and post-concussion. Results revealed significant increases in the speech error categories of pauses and time fillers (interjections/fillers). Additionally, regression analysis showed that a different pattern of errors and disfluencies occur after a sports-related concussion (primarily time fillers) compared to pre-injury (primarily pauses).ConclusionResults demonstrate that speech error rates increase following even mild head injuries, in particular, sports-related concussion. Furthermore, the speech error patterns driving this increase in speech errors, rate of pauses and interjections, are distinct features of this neurological injury, which is in contrast with more severe injuries that are marked by articulation errors and an overall reduction in verbal output. Future studies should consider speech as a diagnostic tool for concussion.

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Speech error rates after a sports-related concussion

Patel

Grabowski²,

Dayalu³

et al. 2023

Front. Psychol.

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A Comparison of Speech Biosignatures of Traumatic Brain Injury and Neurodegeneration (Preprint)

Rubaiat,

Templeton,

Schneider

et al. 2024

Preprint

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BACKGROUND Changes in various speech features have been linked to various neurological and mental health-related pathologies; these changes can often be detected years before a definitive clinical diagnosis. With the growing interest in using speech analysis for detecting a myriad of health conditions and the growing number of patients with multiple health problems, it will be increasingly important to demonstrate that speech analysis can differentiate between these conditions, to provide reliable and accurate diagnosis and assessment. OBJECTIVE Toward this end, this study takes a first step in this direction by examining the speech biosignatures of two common neurological conditions: (1) mild traumatic brain injuries (such as concussions) and (2) neurodegenerative diseases, particularly focusing on amyotrophic lateral sclerosis (ALS) and Parkinson's Disease (PD). METHODS Further, this study utilizes two specific types of speech tests well-known to and frequently used by speech-language pathologists: a diadochokinetic test (“PaTaKa test”) and a sustained vowel test. This work investigates data from over 230 participants and examines 25 temporal and 12 spectral features of sound. We trained classification models using four popular machine learning algorithms including Support Vector Machine, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost) with three speech feature sets selected considering statistical significance based on the analysis of variance (ANOVA). RESULTS The results show that out of these 37 features, over 20 show statistical significance in differentiating between concussions, neurodegenerative diseases, and healthy controls. The paper further compares the performance of different classification models using these features. For the PaTaKa test, when the entire speech feature set was used, Decision Tree obtained the highest F1 score of 0.98 among the four classifiers. When the most significant speech feature set that has ANOVA p-values less than 0.05 was used, the highest F1-score (0.95) was obtained using XGBoost. A maximum F1-score of 0.93 was obtained with both Random Forest and XGBoost when the top statistically significant 5 features were used in model training. None of the classifiers could achieve an F1-score over 0.7 with speech features obtained through sustained vowel test. CONCLUSIONS This study demonstrated that the speech signatures of individuals with concussions, individuals with neurodegenerative conditions, and healthy controls differ in a way that allows us to identify unique patterns in speech associated with each condition, providing opportunities for the development of speech-based biomarkers for early detection and more accurate diagnosis. The findings of this study also indicate that it is feasible to use speech analysis to detect multiple different health conditions within the same person.

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Fluency Changes due to Sports-Related Concussion

Cited by 2 publications

References 34 publications

Speech error rates after a sports-related concussion

Speech error rates after a sports-related concussion

A Comparison of Speech Biosignatures of Traumatic Brain Injury and Neurodegeneration (Preprint)

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