Development of a compact rectenna for wireless powering of a head-mountable deep brain stimulation device

Design of a rectangular spiral planar inverted-F antenna (PIFA) at 915 MHz for wireless power transmission applications is proposed. The antenna and rectifying circuitry form a rectenna, which can produce dc power from a distant radio frequency energy transmitter. The generated dc power is used to operate a low-power deep brain stimulation pulse generator. The proposed antenna has the dimensions of 10 mm × 12.5 mm × 1.5 mm and resonance frequency of 915 MHz with a measured bandwidth of 15 MHz at return loss of -10 dB. A dielectric substrate of FR-4 of εr = 4.8 and δ = 0.015 with thickness of 1.5 mm is used for both antenna and rectifier circuit simulation and fabrication because of its availability and low cost. An L-section impedance matching circuit is used between the PIFA and voltage doubler rectifier. The impedance matching circuit also works as a low-pass filter for elimination of higher order harmonics. Maximum dc voltage at the rectenna output is 7.5 V in free space and this rectenna can drive a deep brain stimulation pulse generator at a distance of 30 cm from a radio frequency energy transmitter, which transmits power of 26.77 dBm.

Protocol-directed sedation versus non-protocol-directed sedation to reduce duration of mechanical ventilation in mechanically ventilated intensive care patients (review)

Background : The sedation needs of critically ill patients have been recognized as a core component of critical care and meeting these is vital to assist recovery and ensure humane treatment. There is growing evidence to suggest that sedation requirements are not always optimally managed. Sub-optimal sedation incorporates both under- and over-sedation and has been linked to both short-term (e.g. length of stay) and long-term (e.g. psychological recovery) outcomes. Various strategies have been proposed to improve sedation management and address aspects of assessment as well as delivery of sedation.

Objectives : To assess the effects of protocol-directed sedation management on the duration of mechanical ventilation and other relevant patient outcomes in mechanically ventilated intensive care unit (ICU) patients. We looked at various outcomes and examined the role of bias in order to examine the level of evidence for this intervention.

Search methods : We searched the Cochrane Central Register of Controlled trials (CENTRAL) (2013; Issue 11), MEDLINE (OvidSP) (1990 to November 2013), EMBASE (OvidSP) (1990 to November 2013), CINAHL (BIREME host) (1990 to November 2013), Database of Abstracts of Reviews of Effects (DARE) (1990 to November 2013), LILACS (1990 to November 2013), Current Controlled Trials and US National Institutes of Health Clinical Research Studies (1990 to November 2013), and reference lists of articles. We re-ran the search in October 2014. We will deal with any studies of interest when we update the review.

Selection criteria : We included randomized controlled trials (RCTs) conducted in adult ICUs comparing management with and without protocol-directed sedation.

Data collection and analysis : Two authors screened the titles and abstracts and then the full-text reports identified from our electronic search. We assessed seven domains of potential risk of bias for the included studies. We examined the clinical, methodological and statistical heterogeneity and used the random-effects model for meta-analysis where we considered it appropriate. We calculated the mean difference (MD) for duration of mechanical ventilation and risk ratio (RR) for mortality across studies, with 95% confidence intervals (CI).

Main results : We identified two eligible studies with 633 participants. Both included studies compared the use of protocol-directed sedation, specifically protocols delivered by nurses, with usual care. We rated the risk of selection bias due to random sequence generation low for one study and unclear for one study. The risk of selection bias related to allocation concealment was low for both studies. We also assessed detection and attrition bias as low for both studies while we considered performance bias high due to the inability to blind participants and clinicians in both studies. Risk due to other sources of bias, such as potential for contamination between groups and reporting bias, was considered unclear. There was no clear evidence of differences in duration of mechanical ventilation (MD -5.74 hours, 95% CI -62.01 to 50.53, low quality evidence), ICU length of stay (MD -0.62 days, 95% CI -2.97 to 1.73) and hospital length of stay (MD -3.78 days, 95% CI -8.54 to 0.97) between people being managed with protocol-directed sedation versus usual care. Similarly, there was no clear evidence of difference in hospital mortality between the two groups (RR 0.96, 95% CI 0.71 to 1.31, low quality evidence). ICU mortality was only reported in one study preventing pooling of data. There was no clear evidence of difference in the incidence of tracheostomy (RR 0.77, 95% CI 0.31 to 1.89). The studies reported few adverse event outcomes; one study reported self extubation while the other study reported re-intubation; given this difference in outcomes, pooling of data was not possible. There was significant heterogeneity between studies for duration of mechanical ventilation (I2 = 86%, P value = 0.008), ICU length of stay (I2 = 82%, P value = 0.02) and incidence of tracheostomy (I2 = 76%, P value = 0.04), with one study finding a reduction in duration of mechanical ventilation and incidence of tracheostomy and the other study finding no difference.

Authors' conclusions : There is currently insufficient evidence to evaluate the effectiveness of protocol-directed sedation. Results from the two RCTs were conflicting, resulting in the quality of the body of evidence as a whole being assessed as low. Further studies, taking into account contextual and clinician characteristics in different ICU environments, are necessary to inform future practice. Methodological strategies to reduce the risk of bias need to be considered in future studies.

Sky high, skin deep: dark technologies of mediation

Media has become responsive architecture. Intelligent media artefacts are now embedded into the very fabric of our existence; they have become the structure of society itself. Ubiquitous computing creates informational environments in which material structures of communication become alive with agency. McLuhan's light bulb is now everyware: [1] technology that mediates by its mere presence. Pervasive mediation, a combination of mobile networks and systems of material translation such as 3D printers and programmable matter--is our current regime of mediation.

Radio frequency energy harvesting from a feeding source in a passive deep brain stimulation device for murine preclinical research

This paper presents the development of an energy harvesting circuit for use with a head-mountable deep brain stimulation (DBS) device. It consists of a circular planar inverted-F antenna (PIFA) and a Schottky diode-based Cockcroft-Walton 4-voltage rectifier. The PIFA has the volume of π × 10(2) × 1.5 mm(3), resonance frequency of 915 MHz, and bandwidth of 16 MHz (909-925 MHz) at a return loss of -10 dB. The rectifier offers maximum efficiency of 78% for the input power of -5 dBm at a 5 kΩ load resistance. The developed rectenna operates efficiently at 915 MHz for the input power within -15 dBm to +5 dBm. For operating a DBS device, the DC voltage of 2 V is recorded from the rectenna terminal at a distance of 55 cm away from a 26.77 dBm transmitter in free space. An in-vitro test of the DBS device is presented.

Sedation protocols to reduce duration of mechanical ventilation in the ICU: a Cochrane Systematic Review

AIMS: Assess the effects of protocol-directed sedation management on the duration of mechanical ventilation and other relevant patient outcomes in mechanically ventilated intensive care unit patients. BACKGROUND: Sedation is a core component of critical care. Sub-optimal sedation management incorporates both under- and over-sedation and has been linked to poorer patient outcomes. DESIGN: Cochrane systematic review of randomized controlled trials. DATA SOURCES: Cochrane Central Register of Controlled trials, MEDLINE, EMBASE, CINAHL, Database of Abstracts of Reviews of Effects, LILACS, Current Controlled Trials and US National Institutes of Health Clinical Research Studies (1990-November 2013) and reference lists of articles were used. REVIEW METHODS: Randomized controlled trials conducted in intensive care units comparing management with and without protocol-directed sedation were included. Two authors screened titles, abstracts and full-text reports. Potential risk of bias was assessed. Clinical, methodological and statistical heterogeneity were examined and the random-effects model used for meta-analysis where appropriate. Mean difference for duration of mechanical ventilation and risk ratio for mortality, with 95% confidence intervals, were calculated. RESULTS: Two eligible studies with 633 participants comparing protocol-directed sedation delivered by nurses vs. usual care were identified. There was no evidence of differences in duration of mechanical ventilation or hospital mortality. There was statistically significant heterogeneity between studies for duration of mechanical ventilation. CONCLUSIONS: There is insufficient evidence to evaluate the effectiveness of protocol-directed sedation as results from the two randomized controlled trials were conflicting.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.