Digital orbitoplethysmograph: A new device to study the regional cerebral circulation using extraorbital plethysmography

Bezrouk, Aleš; Hosszú, Tomáš; Falzon, Owen; Voda, Petr; Vachek, Mikuláš; Zahora, Jiri; Mašín, Vladimír; Camilleri, Kenneth P.; Kremláček, Jan

doi:10.1016/j.jneumeth.2019.108459

Cited by 1 publication

(2 citation statements)

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“…The methods of noninvasive measurement of ICP include optic nerve sheath diameter measurement (space between optic nerve and the surrounding sheath using, e.g. optical coherent tomography) [113], two-depth ophthalmic artery Doppler ultrasonography [114] or digital orbitoplethysmography [115]. Optic nerve sheath diameter has been found to be increased in female collegiate soccer players at the end of a season [116].…”

Section: Cerebral Circulation Evaluationmentioning

confidence: 99%

“…Few commercially available wearable devices for extra orbital plethysmography currently exist on the market. Instead, a custom goggle-based device was developed in [115], which can provide pulse wave results of left and right eyes with high reliability (>85%) in clinically relevant situations and can serve as a promising indirect measurement of ICP.…”

Section: Extra Orbital Plethysmographymentioning

confidence: 99%

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

et al. 2021

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Mild 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.

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