Articulatory dynamics and coordination in classifying cognitive change with preclinical mTBI

Helfer, Brian S.; Quatieri, Thomas F.; Williamson, James R.; Keyes, Laurel; Evans, Benjamin; Greene, W. Nicholas; Vian, Trina; Lacirignola, Joseph; Shenk, Trey E.; Talavage, Thomas M.; Palmer, Jeff; Heaton, Kristin J.

doi:10.21437/interspeech.2014-121

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…The studies in this management phase of SRC are diverse in terms of methodological approach, sample and aim. Three of the 10 studies focus on the role of biomarkers in the prediction of SRC (Helfer et al, 2014 ; Seeger et al, 2020 ; Fedorchak et al, 2021 ). For instance, Seeger et al ( 2020 ) studied youth ice hockey participants (aged 12–17 years) to explore cytokine alterations following pediatric SRC.…”

Section: Resultsmentioning

confidence: 99%

“…Overall, only one of the 10 studies categorized as prediction and prevention apply a deep learning approach (Domel et al, 2021 ), while the remaining 9 are conducted using machine learning algorithms, such as RReliefF (Seeger et al, 2020 ), XGBoost (DiCesare et al, 2020 ), Support Vector Machine (SVM) (Helfer et al, 2014 ; Reynolds et al, 2018 ) and Adaboost (Gabler et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…These sports include American football, ice hockey, soccer, basketball, lacrosse, rugby, volleyball, water polo and handball. The most prominent team sport is American football, with seven studies dedicated to this sport (see Table 2 ), as well as being included in 9 of 11 studies with mixed sports (Cao et al, 2008 ; Helfer et al, 2014 ; Tremblay et al, 2017 ; Reynolds et al, 2018 ; Bergeron et al, 2019 ; Wu et al, 2019 ; Raji et al, 2020 ; Bazarian et al, 2021 ; Castellanos et al, 2021 ). While many individual sports are included in the mixed samples, such as track and field (Helfer et al, 2014 ; Castellanos et al, 2021 ), mixed martial arts (Wu et al, 2019 ), snowboard (Visscher et al, 2019 ), skiing (Raji et al, 2020 ), tennis (Castellanos et al, 2021 ) and wrestling (Bergeron et al, 2019 ), there is only one individual sport with its own study: boxing (Falcone et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

“…As seen in Table 3 , there is a large increase in the number of articles published from 2016 and onwards. Only one article was published before 2011 (Cao et al, 2008 ) and only two were published between 2012 and 2015 (Falcone et al, 2013 ; Helfer et al, 2014 ). Looking at the scholarly field represented in these studies, Table 3 shows that research on machine learning and SRC is mainly published in engineering and medical journals.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion

Tjønndal

Røsten

2022

Front. Sports Act. Living

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Sports injury prevention is an important part of the athlete welfare and safeguarding research field. In sports injury prevention, sport-related concussion (SRC) has proved to be one of the most difficult and complex injuries to manage in terms of prevention, diagnosis, classification, treatment and rehabilitation. SRC can cause long-term health issues and is a commonly reported injury in both adult and youth athletes around the world. Despite increased knowledge of the prevalence of SRC, very few tools are available for diagnosing SRC in athletic settings. Recent technological innovations have resulted in different machine learning and deep learning methodologies being tested to improve the management of this complex sports injury. The purpose of this article is to summarize and map the existing research literature on the use of machine learning in the management of SRC, ascertain where there are gaps in the existing research and identify recommendations for future research. This is explored through a scoping review. A systematic search in the three electronic databases SPORTDiscus, PubMed and Scopus identified an initial 522 studies, of which 24 were included in the final review, the majority of which focused on machine learning for the prediction and prevention of SRC (N = 10), or machine learning for the diagnosis and classification of SRC (N = 11). Only 3 studies explored machine learning approaches for the treatment and rehabilitation of SRC. A main finding is that current research highlights promising practical uses (e.g., more accurate and rapid injury assessment or return-to-sport participation criteria) of machine learning in the management of SRC. The review also revealed a narrow research focus in the existing literature. As current research is primarily conducted on male adolescents or adults from team sports in North America there is an urgent need to include wider demographics in more diverse samples and sports contexts in the machine learning algorithms. If research datasets continue to be based on narrow samples of athletes, the development of any new diagnostic and predictive tools for SRC emerging from this research will be at risk. Today, these risks appear to mainly affect the health and safety of female athletes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion

Tjønndal

Røsten

2022

Front. Sports Act. Living

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Syllabic Diadochokinesis in Adults With and Without Traumatic Brain Injury: Severity, Stability, and Speech Considerations

Solomon

Brungart

Wince

et al. 2021

Am J Speech Lang Pathol

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Purpose Syllabic diadochokinesis (DDK) is a standard assessment task for motor speech disorders. This study aimed to compare rate and regularity of DDK according to the presence or absence of traumatic brain injury (TBI) and severity of TBI, examine the stability of DDK over time, and explore associations between DDK and extemporaneous speech. Method Military service members and veterans were categorized into three groups: no history of TBI (control), uncomplicated mild TBI (mTBI), and moderate through severe (including penetrating) TBI (msTBI). Participants produced rapid alternating-motion and sequential-motion syllable repetitions during one or two sessions. A semi-automated protocol determined syllabic rate and regularity. Perceptual ratings of selected participants' connected speech samples were compared to DDK results. Results Two hundred sixty-three service members and veterans provided data from one session and 69 from two sessions separated by 1.9 years ( SD = 1.0). DDKs were significantly slower overall for mTBI and msTBI groups compared to controls. Regularity of productions did not differ significantly across groups. A significant Group × Task interaction revealed that the msTBI group produced sequential-motion syllable repetitions but not alternating-motion repetitions with greater regularity, whereas the opposite occurred for control and mTBI groups. DDK results did not differ significantly between sessions. Perceptual speech analysis for 30 participants, including 20 with atypical or questionable DDK performance, revealed two participants with mildly abnormal speech. Conclusions Overall, DDK productions are slower than normal in adults with moderate, severe, and penetrating TBI and are stable over time. Regularity of productions did not differentiate groups, although this result differed according to task. There were surprisingly few people identified with disordered speech, making comparisons to DDK data tenuous, and indicating that dysarthria is a rare complication in a population of adults with mostly uncomplicated mTBI who are not selected from referrals to a speech-language pathology clinic.

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The Effects of Symptom Onset Location on Automatic Amyotrophic Lateral Sclerosis Detection Using the Correlation Structure of Articulatory Movements

Wisler

Teplansky

Heitzman

et al. 2021

J Speech Lang Hear Res

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Purpose Kinematic measurements of speech have demonstrated some success in automatic detection of early symptoms of amyotrophic lateral sclerosis (ALS). In this study, we examined how the region of symptom onset (bulbar vs. spinal) affects the ability of data-driven models to detect ALS. Method We used a correlation structure of articulatory movements combined with a machine learning model (i.e., artificial neural network) to detect differences between people with ALS and healthy controls. The performance of this system was evaluated separately for participants with bulbar onset and spinal onset to examine how region of onset affects classification performance. We then performed a regression analysis to examine how different severity measures and region of onset affects model performance. Results The proposed model was significantly more accurate in classifying the bulbar-onset participants, achieving an area under the curve of 0.809 relative to the 0.674 achieved for spinal-onset participants. The regression analysis, however, found that differences in classifier performance across participants were better explained by their speech performance (intelligible speaking rate), and no significant differences were observed based on region of onset when intelligible speaking rate was accounted for. Conclusions Although we found a significant difference in the model's ability to detect ALS depending on the region of onset, this disparity can be primarily explained by observable differences in speech motor symptoms. Thus, when the severity of speech symptoms (e.g., intelligible speaking rate) was accounted for, symptom onset location did not affect the proposed computational model's ability to detect ALS.

show abstract

Articulatory dynamics and coordination in classifying cognitive change with preclinical mTBI

Cited by 17 publications

References 7 publications

Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion

Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion

Syllabic Diadochokinesis in Adults With and Without Traumatic Brain Injury: Severity, Stability, and Speech Considerations

The Effects of Symptom Onset Location on Automatic Amyotrophic Lateral Sclerosis Detection Using the Correlation Structure of Articulatory Movements

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