Low-cost portable EEG device for bridging the diagnostic gap in resource-limited areas.

OBJECTIVE: To develop a low-cost portable EEG system, with real-time automated guidance, for application in resource-limited areas, to bridge the diagnostic and treatment gap. METHODS: We designed, developed, and produced a low-cost system, which records 27-channel EEG plus ECG and streams the signals to an application on a smartphone, which assesses the quality of the signal and gives feedback to the inexperienced user to correct the poor quality signals and reduce artifacts. The application guides the inexperienced user through the steps of recording routine clinical EEG. The recordings are uploaded to a secure cloud, for telemedicine applications. We recruited 10 participants without prior experience with recording EEG. After a brief training session, the participants recorded EEGs following the guidance from the app, without help from human experts. We assessed the usability of the system, with the System Usability Scale (SUS), and we evaluated the impedances and signal quality of the test EEGs recorded by the inexperienced users. RESULTS: All users completed the test EEG recordings, and none of the recordings were of insufficient quality for clinical use. The SUS score was 90.3 ± 6.8, and the average quality rating was 8.04. SIGNIFICANCE: The low-cost, portable EEG system, which uses automated, real-time guidance for conducting EEG recordings, enables inexperienced users to record EEGs of a quality sufficient for clinical applications. This system has the potential to provide EEG services in resource-limited areas, and thereby help bridge the diagnostic and therapeutic gap.

Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria.

BACKGROUND: Our goal was to compare the strength of association and predictive ability of qualitative and quantitative electroencephalographic (EEG) factors with the outcomes of death and neurological disability in pediatric cerebral malaria (CM). METHODS: We enrolled children with a clinical diagnosis of CM admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2012 and 2017. A routine-length EEG was performed within four hours of admission. EEG data were independently interpreted using qualitative and quantitative methods by trained pediatric neurophysiologists. EEG interpreters were unaware of patient discharge outcome. RESULTS: EEG tracings from 194 patients were reviewed. Multivariate modeling revealed several qualitative and quantitative EEG variables that were independently associated with outcomes. Quantitative methods modeled on mortality had better goodness of fit than qualitative ones. When modeled on neurological morbidity in survivors, goodness of fit was better for qualitative methods. When the probabilities of an adverse outcome were calculated using multivariate regression coefficients, only the model of quantitative EEG variables regressed on the neurological sequelae outcome showed clear separation between outcome groups. CONCLUSIONS: Multiple qualitative and quantitative EEG factors are associated with outcomes in pediatric CM. It may be possible to use quantitative EEG factors to create automated methods of study interpretation that have similar predictive abilities for outcomes as human-based interpreters, a rare resource in many malaria-endemic areas. Our results provide a proof-of-concept starting point for the development of quantitative EEG interpretation and prediction methodologies useful in resource-limited settings.

Design of a novel photosensitive phantom for the accurate calibration of the temporal response of electroencephalography systems.

This paper describes a novel method to measure the temporal latency of electroencephalography (EEG) systems using a customized photosensitive phantom. The system was evaluated with three different EEG devices, a medical grade (g.Tec), a consumer grade (Emotiv), and a low-cost device (Arduino SpikerShield). The temporal latencies of the three EEG devices were measured. The proposed method can be easily adapted to assess other EEG devices. The measurements obtained in this experiment provided concrete data for future experiments where accurate timing data are critical.

Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring.

OBJECTIVE: To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication. METHODS: We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life. Good outcome was defined as a Cerebral Performance Category (CPC) score of 1-2 and poor outcome as CPC of 3-5. RESULTS: An improvement in specificity for poor outcome prediction of 4.2% would be sufficient to make continuous EEG monitoring cost-effective (baseline AANPP specificity = 83.9%). In sensitivity analysis, the effect of increased sensitivity on the cost-effectiveness of EEG depends on the utility (u) assigned to a poor outcome. For patients who regard surviving with a poor outcome (CPC 3-4) worse than death (u = -0.34), an increased sensitivity for poor outcome prediction of 13.8% would make AANPP + EEG monitoring cost-effective (baseline AANPP sensitivity = 76.3%). In the closed system, an improvement in sensitivity of 1.8% together with an improvement in specificity of 3% was sufficient to make AANPP + EEG monitoring cost-effective, assuming lifetime return of 50% (USD $70,687). CONCLUSION: Incorporating continuous EEG monitoring into cardiac arrest prognostication is cost-effective if relatively small improvements in sensitivity and specificity are achieved.

Socioeconomic disparities in patients with psychogenic nonepileptic seizures and their caregivers.

OBJECTIVE: There is no information on disparities of patients with psychogenic nonepileptic seizures (PNES) and their caregivers. The objective of this exploratory study is to compare patients with PNES and caregivers with low socioeconomic status (SES) with those of high SES for disparities in healthcare use, seizures, medication adverse effects, psychosocial impact, and knowledge about epilepsy. METHODS: Patients with PNES and caregivers completed surveys about the aforementioned outcomes during their Epilepsy Monitoring Unit (EMU) admission. Associations were evaluated using SES as a binary independent variable and the patient- and caregiver-related outcomes as dependent variables. RESULTS: Forty-three patients and 28 caregivers were recruited. The majority of patients were on average 36 years old, single women, unemployed, with some college education. The majority had PNES for 8 years averaging 20 seizures per month and were maintained on ≥2 antiepileptic drugs (AEDs) prior to their EMU admission. Most caregivers were first-degree relatives with a mean age of 43 years, married employed women of higher educational attainment, typically cohabitating with the patients. Low SES patients showed poorer knowledge about epilepsy (p < 0.0001) and higher anxiety levels (p = 0.03). Conversely, high SES patients demonstrated poorer social functioning (p = 0.04). High SES caregivers showed higher caregiving burden (p = 0.01). CONCLUSION: There are noteworthy disparities in patients with PNES of different SES and their caregivers. Identification of those disparities is a critical step in the creation of appropriate interventions to address them.

48-hour video-EEG monitoring for epilepsy presurgical evaluation is cost-effective and safe in resource-limited setting.

INTRODUCTION: Video-EEG monitoring is one of the key investigations in epilepsy pre-surgical evaluation but limited by cost. This study aimed to determine the efficacy and safety of a 48-hour (3-day) video EEG monitoring, with rapid pre-monitoring antiepileptic drugs withdrawal. MATERIAL AND METHODS: This is a retrospective study of epilepsy cases with VEM performed in University Malaya Medical Center (UMMC), Kuala Lumpur, from January 2012 till August 2016. RESULTS: A total of 137 cases were included. The mean age was 34.5 years old (range 15-62) and 76 (55.8 %) were male. On the first 24 -h of recording (D1), 81 cases (59.1 %) had seizure occurrence, and 109 (79.6 %) by day 2 (D2). One-hundred and nine VEMs (79.6 %) were diagnostic, in guiding surgical decision or further investigations. Of these, 21 had less than 2 seizures recorded in the first 48 h but were considered as diagnostic because of concordant interictal ± ictal activities, or a diagnosis such as psychogenic non-epileptic seizure was made. Twenty-eight patients had extension of VEM for another 24-48 h, and 11 developed seizures during the extension period. Extra-temporal lobe epilepsy and seizure frequency were significant predictors for diagnostic 48 -h VEM. Three patients developed complications, including status epilepticus required anaesthetic agents (1), seizure clusters (2) with postictal psychosis or dysphasia, and all recovered subsequently. CONCLUSIONS: 48-h video EEG monitoring is cost-effective in resource limited setting.

Efficacy and economic evaluation of delivery of care with tele-continuous EEG in critically ill patients: a multicentre, randomised controlled trial (Tele-cRCT) study protocol.

INTRODUCTION: Some critically ill patients are confirmed by continuous electroencephalography (cEEG) monitoring that non-convulsive seizure (NCS) and/or non-convulsive status epilepticus (NCSE) are causes of their depressed level of consciousness. Shortage of epilepsy specialists, especially in developing countries, is a major limiting factor in implementing cEEG in general practice. Delivery of care with tele-continous EEG (tele-cEEG) may be a potential solution as this allows specialists from a central facility to remotely assist local neurologists from distant areas in interpreting EEG findings and suggest proper treatment. No tele-cEEG programme has been implemented to help improve quality of care. Therefore, this study is conducted to assess the efficacy and cost utility of implementing tele-cEEG in critical care. METHODS AND ANALYSIS: The Tele-cRCT study is a 3-year prospective, randomised, controlled, parallel, multicentre, superiority trial comparing delivery of care through 'Tele-cEEG' intervention with 'Tele-routine EEG (Tele-rEEG)' in patients with clinical suspicion of NCS/NCSE. A group of EEG specialists and a tele-EEG system were set up to remotely interpret EEG findings in six regional government hospitals across Thailand. The primary outcomes are functional neurological outcome (modified Rankin Scale, mRS), mortality rate and incidence of seizures. The secondary outcomes are cost utility, length of stay, emergency visit/readmission, impact on changing medical decisions and health professionals' perceptions about tele-cEEG implementation. Functional outcome (mRS) will be assessed at 3 and 7 days after recruitment, and again at time of hospital discharge, and at 90 days, 6 months, 9 months and 1 year. Costs and health-related quality of life will be assessed using the Thai version of the EuroQol-five dimensions-five levels (EQ-5D-5L) at hospital discharge, and at 90 days, 6 months, 9 months and 1 year. ETHICS AND DISSEMINATION: This study has been approved by the ethics committees of the Faculty of Medicine, Chulalongkorn University, and of Ramathibodi Hospital, Mahidol University, and registered on Thai Clinical Trials Registry. The results will be disseminated in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: TCTR20181022002; preresults.

Doing More with Less: A Minimally Invasive, Cost-Conscious Approach to Stereoelectroencephalography.

BACKGROUND: Stereoelectroencephalography (SEEG) is a commonly used technique for mapping the epileptogenic zone before epilepsy surgery. Many SEEG depth electrode implantation techniques involve the use of extensive technological equipment and shaving of the patient's entire head before electrode implantation. Our goal was to evaluate an SEEG depth electrode implantation technique that used readily available cost-effective neurosurgical equipment, was minimally invasive in nature, and required negligible hair shaving. METHODS: Data on demographic characteristics, operative time, hemorrhagic complications, implantation complications, infection, morbidity, and mortality among patients who underwent this procedure were reviewed retrospectively. RESULTS: Between April 2016 and March 2018, 23 patients underwent implantation of 213 depth electrodes with use of this technique. Mean (SD) operative time was 123 (32) minutes (range, 66-181 minutes). A mean (SD) of 9.3 (1.4) electrodes were placed for each patient (range, 8-13 electrodes). Two of the 213 electrodes (0.9%) were associated with postimplantation asymptomatic hemorrhage. One of the 213 electrodes (0.5%) was placed extradurally or incorrectly. None of the 213 electrodes was associated with symptomatic complications. No patients experienced infectious complications at any point in the preoperative, perioperative, or postoperative stages. CONCLUSIONS: This minimally invasive, cost-effective technique for SEEG depth electrode implantation is a safe, efficient method that uses readily available basic neurosurgical equipment. This technique may be useful in neurosurgery centers with more limited resources. This study suggests that leaving the patient's hair largely intact throughout the procedure does not pose an additional infection risk.

Yield and cost-effectiveness of tilt table tests combined with video-EEG.

To study the outcomes of a series of consecutive tilt table tests combined with video-EEG (TTVE) at a single center, and assess their cost-effectiveness compared with other neurophysiological tests. We retrospectively reviewed medical records of patients who underwent TTVE studies between March 1st, 2013 to April 1st, 2018. Detailed clinical history, including patient demographics, reasons for referral, anti-seizure medications, and neurophysiological studies obtained prior to the TTVE studies were extracted from chart reviews. The fee for each neurophysiological test was identified from the Centers for Medicare & Medicaid Services. Fifty-two patients underwent TTVE studies. Thirteen patients (25%) were diagnosed with vasovagal syncope, two (3.8%) were diagnosed with postural orthostatic tachycardia syndrome, and three (5.8%) had psychogenic non-epileptic events during the test. Four out of 12 patients stopped anti-seizure medication(s) after the TTVE. Prior to referral for TTVE, an average of $3,748 per person was spent on neurophysiological tests, which were inconclusive. The average fee for one TTVE test was $535.32, and the fee per test affecting diagnosis or management (defined as the cost divided by the yield of the test) was $1,547. The TTVE test is cost-effective in evaluating refractory episodes of loss of consciousness, atypical of epileptic seizures. In addition to diagnosing syncope, TTVE can be valuable in identifying psychogenic events.

Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study.

The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.

The real-world economic impact of home-based video electroencephalography: the payer perspective.

Aims: Electroencephalography (EEG) is an established method to evaluate and manage epilepsy; video EEG (VEEG) has significantly improved its diagnostic value. This study compared healthcare costs and diagnostic-related outcomes associated with outpatient vs inpatient VEEG among patients with epilepsy in the US. Materials and methods: This study used Truven MarketScan Commercial and Medicare Supplemental claims databases. Patients with a VEEG between July 1, 2013 and December 31, 2016 were identified. Index event was the first VEEG claim, which was used to determine inpatient and outpatient cohorts. Continuous health plan enrollment 6 months pre- and 12 months post-index VEEG was required. Primary outcomes were costs during the index event and 12 months post index. A generalized linear model with gamma distribution and a log link was used to estimate adjusted index and post-index costs. Results: Controlling for baseline differences, epilepsy-related cost of index VEEG was significantly lower for the outpatient ($4,098) vs the inpatient cohort ($13,821; p < 0.0001). The cost differences observed at index were maintained in the post-index period. The 12-month post-index epilepsy-related costs were lower in the outpatient cohort ($6,114 vs $12,733, p < 0.0001). Time from physician referral to index VEEG was significantly shorter in the outpatient cohort (30.6 vs 42.5 days). Patients in the inpatient cohort were also more likely to undergo an additional subsequent follow-up inpatient VEEG (p < 0.0001). Limitations: Administrative claims data have limitations, including lack of data on clinical presentation, disease severity, and comprehensive health plan information. Generalizability may be limited to a US insured population of patients who met study criteria. Conclusions: Index VEEG was less costly in an outpatient vs inpatient cohort, and costs were lower during the follow-up period of 12 months, suggesting that outpatient VEEG can be provided to appropriate patients as a less costly option. There were fewer follow-up tests in the outpatient cohort with similar pre- and post-index diagnoses.

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System.

This paper presents an open source framework called Creamino. It consists of an Arduino-based cost-effective quick-setup EEG platform built with off-the-shelf components and a set of software modules that easily allow users to connect this system to Simulink or BCI-oriented tools (such as BCI2000 or OpenViBE) and set up a wide number of neuroscientific experiments. Creamino is capable of processing multiple EEG channels in real-time and operates under Windows, Linux, and Mac OS X in real-time on a standard PC. Its objective is to provide a system that can be readily fabricated and used for neurophysiological experiments and, at the same time, can serve as the basis for development of novel BCI platforms by accessing and modifying its open source hardware and software libraries. Schematics, gerber files, bill of materials, source code, software modules, demonstration videos, and instructions on how to use these modules are available free of charge for research and educational purposes online at https://github.com/ArcesUnibo/creamino. Application cases show how the system can be used for neuroscientific or BCI experiments. Thanks to its low production cost and its compatibility with open-source BCI tools, the system presented is particularly suitable for use in BCI research and educational applications.

Stereoelectroencephalography for Refractory Epileptic Patients Considered for Surgery: Systematic Review, Meta-Analysis, and Economic Evaluation.

BACKGROUND: Stereoelectroencephalography (SEEG) has been shown to be a valuable tool for the anatomoelectroclinical definition of the epileptogenic zone (EZ) in patients with medically refractory epilepsy considered for surgery (RES patients). In Spain, many of those patients are not offered this diagnostic procedure. OBJECTIVE: To evaluate the effectiveness, safety, and cost-effectiveness of SEEG to define the EZ in RES patients compared to no SEEG intervention, ie, remaining with further antiepileptic drugs. METHODS: We undertook a systematic review with meta-analyses on the effectiveness and safety of SEEG. A cost-effectiveness analysis was conducted using a Markov model, which simulates the costs and health outcomes of individuals for a lifetime horizon from the perspective of the Spanish National Health Service. The effectiveness measure was quality-adjusted life years (QALYs). We ran extensive sensitivity analyses, including a probabilistic sensitivity analysis. RESULTS: The EZ was found in 92% of patients who underwent SEEG (95% confidence interval [CI]: 0.87-0.96); 72% were eligible for epilepsy surgery (95% CI: 0.66-0.78) and 33% were free of seizures after surgery (95% CI: 0.27-0.42). Of the patients who underwent surgery, 47% were free of seizures after surgery (95% CI: 0.37-0.58). Complications related to implantation and monitoring of SEEG and the subsequent intervention occurred in 1.3% of patients (95% CI: 0.01-0.02). In the base case analysis, SEEG led to higher QALYs and healthcare costs with an estimated incremental cost-effectiveness ratio of 10 368 EUR per QALY (95% CI: dominant-113 911), making the probability of cost-effectiveness between 75% and 88%. Further sensitivity analyses showed that the results of the study were robust. CONCLUSION: SEEG is a cost-effective technology in RES patients when compared to no SEEG intervention.

Deep compressive autoencoder for action potential compression in large-scale neural recording.

OBJECTIVE: Understanding the coordinated activity underlying brain computations requires large-scale, simultaneous recordings from distributed neuronal structures at a cellular-level resolution. One major hurdle to design high-bandwidth, high-precision, large-scale neural interfaces lies in the formidable data streams (tens to hundreds of Gbps) that are generated by the recorder chip and need to be online transferred to a remote computer. The data rates can require hundreds to thousands of I/O pads on the recorder chip and power consumption on the order of Watts for data streaming alone. One of the solutions is to reduce the bandwidth of neural signals before transmission. APPROACH: We developed a deep learning-based compression model to reduce the data rate of multichannel action potentials. The proposed compression model is built upon a deep compressive autoencoder (CAE) with discrete latent embeddings. The encoder network of CAE is equipped with residual transformations to extract representative features from spikes, which are mapped into the latent embedding space and updated via vector quantization (VQ). The indexes of VQ codebook are further entropy coded as the compressed signals. The decoder network reconstructs spike waveforms with high quality from the quantized latent embeddings through stacked deconvolution. MAIN RESULTS: Extensive experimental results on both synthetic and in vivo datasets show that the proposed model consistently outperforms conventional methods that utilize hand-crafted features and/or signal-agnostic transformations and compressive sensing by achieving much higher compression ratios (20-500×) and better or comparable reconstruction accuracies. Testing results also indicate that CAE is robust against a diverse range of imperfections, such as waveform variation and spike misalignment, and has minor influence on spike sorting accuracy. Furthermore, we have estimated the hardware cost and real-time performance of CAE and shown that it could support thousands of recording channels simultaneously without excessive power/heat dissipation. SIGNIFICANCE: The proposed model can reduce the required data transmission bandwidth in large-scale recording experiments and maintain good signal qualities, which will be helpful to design power-efficient and lightweight wireless neural interfaces. We have open sourced the code implementation of the work at https://github.com/tong-wu-umn/spike-compression-autoencoder.

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.

The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75⁻92%) was obtained in identifying P300 evoked potentials.

An embedded implementation based on adaptive filter bank for brain-computer interface systems.

BACKGROUND: Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. NEW-METHOD: This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. RESULTS: The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. COMPARISON-WITH-EXISTING-METHOD: Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. CONCLUSIONS: Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates.

Does Continuous Video-EEG in Patients With Altered Consciousness Improve Patient Outcome? Current Evidence and Randomized Controlled Trial Design.

Continuous video-EEG is recommended for patients with altered consciousness; as compared to routine EEG (lasting <30 minutes), it improves seizure detection, but is time- and resource-consuming. Although North American centers increasingly implement continuous video-EEG, most other (including European) hospitals have insufficient resources. Only one study suggested that continuous video-EEG could improve outcome in adults, and recent assessments challenge this view. This article reviews current evidence on the added value for continuous video-EEG in clinical terms and describes a design for a prospective study.In a multicenter randomized clinical trial (NCT03129438), adults with a Glasgow Coma Scale ≤11 will be randomized 1:1 to continuous video-EEG (cEEG) for 30 to 48 hours or 2 routine EEG (rEEG), assessed through standardized American Clinical Neurophysiology Society (ACNS) guidelines. The primary outcome will be mortality at 6 months, assessed blindly. Secondary outcomes will explore functional status at 4 weeks and 6 months, intensive care unit (ICU) length of stay, infection rates, and hospitalization costs. Using a 2-sided approach with power of 0.8 and a error of 0.05, 2 × 174 patients are needed to detect an absolute survival difference of 14%, suggested by the single available study on the topic.This study should help clarifying whether cEEG has a significant impact on outcome and define its cost effectiveness. If the trial will result positive, it will encourage broader implementation of cEEG with consecutive substantial impact on health care and resource allocations. If not, it may offer a rationale to design a larger trial, and - at least for smaller centers - to avoid widespread implementation of cEEG, rationalizing personnel and device costs.

Screening for Sturge-Weber syndrome: A state-of-the-art review.

Infants with a high-risk distribution of port-wine stains are commonly screened for Sturge-Weber syndrome using brain magnetic resonance imaging. There is no consensus about which port-wine stain phenotypes to screen, optimal timing, screening sensitivity, or whether presymptomatic diagnosis improves neurodevelopmental outcomes. This state-of-the-art review examines the evidence in favor of screening for Sturge-Weber syndrome, based on its effect on neurodevelopmental outcomes, against the risks and limitations of screening magnetic resonance imaging and electroencephalography. A literature search of PubMed/MEDLINE was conducted between January 2005 and May 2017 using key search terms. Relevant articles published in English were reviewed; 34 articles meeting the search criteria were analyzed according to the following outcome measures: neurodevelopmental outcome benefit of screening, diagnostic yield, financial costs, procedural risks, and limitations of screening magnetic resonance imaging and electroencephalography. There is no evidence that a presymptomatic Sturge-Weber syndrome diagnosis with magnetic resonance imaging results in better neurodevelopmental outcomes. The utility of electroencephalographic screening is also unestablished. In Sturge-Weber syndrome, neurodevelopmental outcomes depend on prompt recognition of neurologic red flags and early seizure control. Small numbers and a lack of prospective randomized controlled trials limit these findings. For infants with port-wine stain involving skin derived from the frontonasal placode (forehead and hemifacial phenotypes), we recommend early referral to a pediatric neurologist for parental education, counselling, and monitoring for neurologic red flags and seizures and consideration of electroencephalography regardless of whether magnetic resonance imaging is performed or its findings.

Implementation of Continuous Video-Electroencephalography at a Community Hospital Enhances Care and Reduces Costs.

BACKGROUND: Despite data indicating the importance of continuous video-electroencephalography (cvEEG) monitoring, adoption has been slow outside major academic centers. Barriers to adoption include the need for technologists, equipment, and cvEEG readers. Advancements in lower-cost lead placement templates and commercial systems with remote review may reduce barriers to allow community centers to implement cvEEG. Here, we report our experience, lessons learned, and financial impact of implementing a community hospital cvEEG-monitoring program. METHODS: We implemented an adult cvEEG service at Duke Regional Hospital (DRH), a community hospital affiliate, in June of 2012. Lead placement templates were used in the implementation to reduce the impact on technologists by using other bedside providers for EEG initiation. Utilization of the service, study quality, and patient outcomes were tracked over a 3-year period following initiation of service. RESULTS: Service was implemented at essentially no cost. Utilization varied from a number of factors: intensive care unit (ICU) attending awareness, limited willingness of bedside providers to perform lead placement, and variation in practice of the consulting neurologists. A total of 92 studies were performed on 88 patients in the first 3 years of the program, 24 in year one, 27 in year two, and 38 in year three, showing progressive adoption. Seizures were seen in 25 patients (27%), 19 were in status, of which 18 were successfully treated. Transfers to the main hospital, Duke University Medical Center, were prevented for 53 patients, producing an estimated cost savings of $145,750. The retained patients produced a direct contribution margin of about $75,000, and the margin was just over $100,000 for the entire monitored cohort. CONCLUSION: ICU cvEEG service is feasible and practical to implement at the community hospital level. Service was initiated at little to no cost and clearly enhanced care, increased breadth of care, increased ICU census, and reduced transfers. The system allowed for successful management of ICU patients with underlying seizures and eliminated interfacility transfers, producing a savings of $145,750. The savings combined with the retained patient revenue produced a total revenue of over $250,000 with additional revenue in professional services as well. These results suggest expansion of cvEEG monitoring to community ICUs is practical, financially sustainable, improves the level and quality of care, and reduces costs.