Background Virtual reality is increasingly being utilized by clinicians to facilitate analgesia and anxiolysis within an inpatient setting. There is however, a lack of a clinically relevant review to guide its use for this purpose. Objective To systematically review the current evidence for the efficacy of virtual reality as an analgesic in the management of acute pain and anxiolysis in an inpatient setting. Methods A comprehensive search was conducted up to and including January 2019 on PubMed, Ovid Medline, EMBASE, and Cochrane Database of Systematic reviews according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Search terms included virtual reality, vr, and pain. Primary articles with a focus on acute pain in the clinical setting were considered for the review. Primary outcome measures included degree of analgesia afforded by virtual reality therapy, degree of anxiolysis afforded by virtual reality therapy, effect of virtual reality on physiological parameters, side effects precipitated by virtual reality, virtual reality content type, and type of equipment utilized. Results Eighteen studies were deemed eligible for inclusion in this systematic review; 67% (12/18) of studies demonstrated significant reductions in pain with the utilization of virtual reality; 44% (8/18) of studies assessed the effects of virtual reality on procedural anxiety, with 50% (4/8) of these demonstrating significant reductions; 28% (5/18) of studies screened for side effects with incidence rates of 0.5% to 8%; 39% (7/18) of studies evaluated the effects of virtual reality on autonomic arousal as a biomarker of pain, with 29% (2/7) demonstrating significant changes; 100% (18/18) of studies utilized a head mounted display to deliver virtual reality therapy, with 50% being in active form (participants interacting with the environment) and 50% being in passive form (participants observing the content only). Conclusions Available evidence suggests that virtual reality therapy can be applied to facilitate analgesia for acute pain in a variety of inpatient settings. Its effects, however, are likely to vary by patient population and indication. This highlights the need for individualized pilot testing of virtual reality therapy’s effects for each specific clinical use case rather than generalizing its use for the broad indication of facilitating analgesia. In addition, virtual reality therapy has the added potential of concurrently providing procedural anxiolysis, thereby improving patient experience and cooperation, while being associated with a low incidence of side effects (nausea, vomiting, eye strain, and dizziness). Furthermore, findings indicated a head mounted display should be utilized to deliver virtual reality therapy in a clinical setting with a slight preference for active over passive virtual reality for analgesia. There, however, appears to be insufficient evidence to substantiate the effect of virtual reality on autonomic arousal, and this should be considered at best to be for investigational uses, at present.

Traffic flow prediction is an important application of the ITS technology. In this paper, we applied non-linear timeseries modeling techniques to analyze a traffic data. Our objective is to investigate the deterministic properties of traffic flow using a nonlinear time series analysis technique. The experiment is performed for inductance loop data collected from the San Antonio freeway system. Our study concludes that the traffic data exhibits chaotic properties and techniques based on phase space dynamics can be used to analyze and predict the traffic flow.

BackgroundNon-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance which is attracting increasing research interest. There is however, only limited data outlining the reference ranges for normal cardiac time intervals (CTIs). The objective of our group was to carry out a systematic review to outline normal fetal CTIs using NIFECG.MethodsA systematic review of peer reviewed literature was performed, searching PUBMED,Ovid MEDLINE and EMBASE. The outcomes of interest included fetal CTIs (P wave duration, PR interval, QRS duration and QT interval) and a descriptive summary of relevant studies as well. The outcomes were grouped as early pre-term (≤ 32 weeks), moderate to late pre-term (32–37 weeks) and term (37–41 weeks).Results8 studies were identified as suitable for inclusion. Reference ranges of CTIs were generated. Both PR interval and QRS duration demonstrated a linear correlation with advancing gestation. Several studies also demonstrated a reduction in signal acquisition between 27 and 32 weeks due to the attenuation by vernix caseosa. In this group, both the P wave and T waves were difficult to detect due to signal strength and interference.ConclusionNIFECG demonstrates utility to quantify CTIs in the fetus, particularly at advanced gestations. Larger prospective studies should be directed towards establishing reliable CTIs across various gestations.

In this paper, we propose a decision-based, signal-adaptive median filtering algorithm for removal of impulse noise. Our algorithm achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image. The notion of homogeneity level is defined for pixel values based on their global and local statistical properties. The cooccurrence matrix technique is used to represent the correlations between a pixel and its neighbors, and to derive the upper and lower bound of the homogeneity level. Noise detection is performed at two stages: noise candidates are first selected using the homogeneity level, and then a refining process follows to eliminate false detections. The noise detection scheme does not use a quantitative decision measure, but uses qualitative structural information, and it is not subject to burdensome computations for optimization of the threshold values. Empirical results indicate that our scheme performs significantly better than other median filters, in terms of noise suppression and detail preservation.

Background Early Health Technology Assessment (EHTA) is an evolving field in health policy which aims to provide decision support and mitigate risk during early medical device innovation. The clinician is a key stakeholder in this process and their role has traditionally been confined to assessing device efficacy and safety alone. There is however, no data exploring their role in this process and how they can contribute towards it. This motivated us to carry out a systematic review to delineate the role of the clinician in EHTA as per the PRISMA guidelines. Methods A systematic search of peer reviewed literature was undertaken across PUBMED, OVID Medline and Web of science up till June 2018. Studies that were suitable for inclusion focused on clinician input in health technology assessment or early medical device innovation . A qualitative approach was utilised to generate themes on how clinicians could contribute in general and specific areas of EHTA. Data was manually extracted by the authors and themes were agreed in consensus using a grounded theory framework. The specific stages included: All stages of EHTA, Basic research on mechanisms, Targeting for specific product, Proof of principle and Prototype and product development. Bias was assessed utilising the NICE Qualitative checklist. Results A total of 33 articles met the inclusion criteria for the review. Areas identified in which the clinicians could contribute to EHTA included: i) needs driven problem solving, ii) conformity assessment of MDs, iii) economic evaluation of MDs and iv) addressing the conflicts in interest. For clinicians’ input across the various specific areas of EHTA, an innovation framework was generated based on the subthemes extracted. Conclusions The following review has identified the various segments in which clinicians can contribute to EHTA to inform stakeholders and has also proposed an innovation framework. Electronic supplementary material The online version of this article (10.1186/s12913-019-4305-9) contains supplementary material, which is available to authorized users.

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external cephalic version (ecV) is associated with a moderate degree of pain. Virtual reality (VR) is a technology that has shown promise in offering procedural analgesia. We undertook a clinical pilot to assess the viability of VR to reduce pain during ECV. In an open randomised controlled trial (RCT), we randomised 50 women to either VR or standard care each (25 per group). Women receiving VR were administered VR content (Skylights) via a headset. Pre-and post-procedural measures of pain, anxiety, device experience and vital signs were measured. There were no significant differences between groups (VR/no VR) in pain scores (60.68 vs 49.76; p = 0.2), ECV success rates (80% vs 76%; p = 0.7) or anxiety levels. The women receiving VR had a significantly higher anticipation of pain pre-procedurally (70.0 vs 50.0; p = 0.03). 20 (80%) of the VR women indicated that they would use VR again and 22 (88%) indicated they would recommend it to a friend having ECV. There were no significant differences between groups for side effects encountered or changes in vital signs. We have shown that using VR during ecV is feasible and appears safe. our results inform the design of future Rcts. open Scientific RepoRtS | (2020) 10:3141 | https://doi.

This paper proposes a new method for detecting P and T waves in multilead ECG based on the Finite Rate of Innovation(FRI) technique [8]. A simple QRS detection scheme will be presented followed by a novel P and T wave detection algorithm. The novelty here is the modelling of the P and T wave using a Gaussian kernel. Using a 2D wavelet decomposition, the approximation coefficients are windowed based on the QRS locations. The FRI method is then used to identify the Gaussian distribution present in the window which will in turn provide the locations of the P and T wave. This method was tested on more than an hour of clean and noisy data and shows good performance in the noisy case.