Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMotion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I MS ). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for I MS ). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.Landspitali University Hospital, Reykjavi

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Publisher Copyright: © 2022, The Author(s).Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.Peer reviewe

Advancing in heart surgical planning by using 3D printing technology

Fyrir skurðaðgerð getur verið erfitt að sjá fyrir sér flókna lífræðilega uppbygginu útfrá tvívíddar myndum. Árangursríkara er að nota nákvæm þrívíddarprentuð módel, sem sýna eiginleika svæðisins sem aðgerð skal gera á. Markmið þessarar rannsóknar er að þróa plan hjartaskurðaðgerða með hjálp þrívíddar líkana. Það er gert með því að finna bestu læknisfræðilegu myndatæknina og bestu aðferðina til að búa til hjarta módelin. Samanburður var gerður á gæðunum á sex mismunandi læknisfræðilegum myndum og hjarta þrívíddar módelum, sem voru búin til útfrá þeim myndum. Tveir verkferlar voru hannaðir til að skapa hjarta módelin. Þessi módel er hægt að nota fyrir margvíslega hjartakvilla. Fyrsti verkferilinn snýst um að búa til líkan af hjarta sem sýnir blóðmagnið inni í hjartanu. Seinni verkferillinn býr til líkan sem sýnir holrýmið inni í hjartanu í smáatriðum. Mestu gæðin voru í hjarta módelum sem voru búin til úr myndum úr sneiðmyndatæki þar sem sjónsviðið var ≤ 220 mm, pixla stærðin ≤ 0.42 mm og þykktin á sneiðunum ≤ 0.75 mm. Með þessum myndum tekur styttri tíma að búa til þrívíddar líkanið og í því sjást meiri líffræðileg smáatriði. Báðir verkferlarnir voru notaðir til að búa til þrívíddar módel í tilfellum sjö sjúklinga. Af þeim eru fjögur tilfelli birt í þessari skýrslu. Allar aðgerðir sem hjarta þrívíddar módelin voru útbúin fyrir heppnuðust vel. Kostirnir við að nota þessi módel voru, minni tími á skurðstofu, betri yfirsýn yfir lífræðilega uppbygginu, aukið sjálfsöryggi læknisinns og betri samskipti við sjúkling. Gæðin eru stöðugt að aukast í læknisfræðilegum myndum og þrívíddarprentunnar tækni. Aukin upplausn í myndum og rauverulegri þrívíddarprentunnar tækni mun valda því að þrívíddar módel verða notuð meira fyrir margvíslegar hjarta aðgerðir. Að öllum líkindum verður lögð meiri áhersla á hagnýt þrívíddarprentuð módel í framtíðinni. Þessi líkön eru líkari hjartanu bæði í útliti, við snertingu og við notkun.Visualising complex anatomy before performing surgeries is often difficult. Having 3D printed models that accurately display the region of interest can enable better visualisation. This study aims to improve heart surgical planning by finding the best imaging technique and segmentation workflow to create 3D printed heart models. A comparison was made between the quality of six medical scans of hearts and the 3D models created from those images. Two new workflows were created for segmentation that can be applied for diverse heart conditions. The first called heart segmentation, which involves visualising the volume of blood inside the heart. The second called heart cavity segmentation is used to visualise the internal cavity of the heart in detail. The best quality 3D printed heart models were made from images taken on a dual-source CT scanner capable of imaging multiple heart phases. The best practice is to have medical images with a pixel size ≤ 0.42 mm, field of view ≤ 220 mm and the slice thickness ≤ 0.75 mm. This provides a less time-consuming segmentation and a detailed 3D printed model. The segmentation workflows were tested on seven patient cases, four of which were displayed in this thesis. All surgical procedures that the 3D printed heart models were created for were performed successfully. The benefits of using these models include decreased surgical time, better anatomical visualisation, increased confidence of the operating surgeon and better patient communication. The quality in both medical imaging and 3D printing is constantly evolving. Better image resolution and a more realistic 3D printing technology will result in a wider spectrum of applications of 3D models in heart surgery. Future applications include detailed functional models, where the models have a more realistic look, feel and function