Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Simple item page
dc.citation.articleNumber041006en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitleJournal of Neural Engineeringen_US
dc.citation.volumeNumber18en_US
dc.contributor.authorSchmid, Williamen_US
dc.contributor.authorFan, Yingyingen_US
dc.contributor.authorChi, Taiyunen_US
dc.contributor.authorGolanov, Eugeneen_US
dc.contributor.authorRegnier-Golanov, Angelique S.en_US
dc.contributor.authorAusterman, Ryan J.en_US
dc.contributor.authorPodell, Kennethen_US
dc.contributor.authorCherukuri, Paulen_US
dc.contributor.authorBentley, Timothyen_US
dc.contributor.authorSteele, Christopher T.en_US
dc.contributor.authorSchodrof, Sarahen_US
dc.contributor.authorAazhang, Behnaamen_US
dc.contributor.authorBritz, Gavin W.en_US
dc.contributor.orgNeuroengineering Initiative (NEI)en_US
dc.date.accessioned2021-09-21T15:37:43Zen_US
dc.date.available2021-09-21T15:37:43Zen_US
dc.date.issued2021en_US
dc.description.abstractMild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.en_US
dc.identifier.citationSchmid, William, Fan, Yingying, Chi, Taiyun, et al.. "Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries." <i>Journal of Neural Engineering,</i> 18, no. 4 (2021) IOP Publishing: https://doi.org/10.1088/1741-2552/ac1982.en_US
dc.identifier.digitalSchmid_2021en_US
dc.identifier.doihttps://doi.org/10.1088/1741-2552/ac1982en_US
dc.identifier.urihttps://hdl.handle.net/1911/111376en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.rightsOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.titleReview of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuriesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Schmid_2021.pdf
Size:
1.13 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
ECE Publications