How accurate do self-reported seizures need to be for effective medication management in epilepsy?

Studies suggest that self-reported seizure diaries suffer from 50% under-reporting on average. It is unknown to what extent this impacts medication management. This study used simulation to predict the seizure outcomes of a large heterogeneous clinic population treated with a standardized algorithm based on self-reported seizures. Using CHOCOLATES, a state-of-the-art realistic seizure diary simulator, 100 000 patients were simulated over 10 years. A standard algorithm for medication management was employed at 3 month intervals for all patients. The impact on true seizure rates, expected seizure rates, and time-to-steady-dose were computed for self-reporting sensitivities 0%-100%. Time-to-steady-dose and medication use mostly did not depend on sensitivity. True seizure rate decreased minimally with increasing self-reporting in a non-linear fashion, with the largest decreases at low sensitivity rates (0%-10%). This study suggests that an extremely wide range of sensitivity will have similar seizure outcomes when patients are clinically treated using an algorithm similar to the one presented. Conversely, patients with sensitivity ≤10% would be expected to benefit (via lower seizure rates) from objective devices that provide even small improvements in seizure sensitivity.

Prospective validation of a seizure diary forecasting falls short.

OBJECTIVE: Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS: We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS: The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

Minimum clinical utility standards for wearable seizure detectors: A simulation study.

OBJECTIVE: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.

Flexible realistic simulation of seizure occurrence recapitulating statistical properties of seizure diaries.

OBJECTIVE: A realistic seizure diary simulator is currently unavailable for many research needs, including clinical trial analysis and evaluation of seizure detection and seizure-forecasting tools. In recent years, important statistical features of seizure diaries have been characterized. These include (1) heterogeneity of individual seizure frequencies, (2) the relation between average seizure rate and standard deviation, (3) multiple risk cycles, (4) seizure clusters, and (5) limitations on inter-seizure intervals. The present study unifies these features into a single model. METHODS: Our approach, Cyclic Heterogeneous Overdispersed Clustered Open-source L-relationship Adjustable Temporally limited E-diary Simulator (CHOCOLATES) is based on a hierarchical model centered on a gamma Poisson generator with several modifiers. This model accounts for the aforementioned statistical properties. The model was validated by simulating 10 000 randomized clinical trials (RCTs) of medication to compare with 23 historical RCTs. Metrics of 50% responder rate (RR50) and median percent change (MPC) were evaluated. We also used CHOCOLATES as input to a seizure-forecasting tool to test the flexibility of the model. We examined the area under the receiver-operating characteristic (ROC) curve (AUC) for test data with and without cycles and clusters. RESULTS: The model recapitulated typical findings in 23 historical RCTs without the necessity of introducing an additional "placebo effect." The model produced the following RR50 values: placebo: 17 ± 4%; drug 38 ± 5%; and the following MPC values: placebo: 13 ± 6%; drug 40 ± 4%. These values are similar to historical data: for RR50: placebo, 21 ± 10%, drug: 43 ± 13%; and for MPC: placebo: 17 ± 10%, drug: 41 ± 11%. The seizure forecasts achieved an AUC of 0.68 with cycles and clusters, whereas without them the AUC was 0.51. SIGNIFICANCE: CHOCOLATES represents the most realistic seizure occurrence simulator to date, based on observations from thousands of patients in different contexts. This tool is open source and flexible, and can be used for many applications, including clinical trial simulation and testing of seizure-forecasting tools.

Quantifying and controlling the impact of regression to the mean on randomized controlled trials in epilepsy.

OBJECTIVE: Randomized controlled trials (RCTs) in epilepsy for drug treatments are plagued by high costs. One potential remedy is to reduce placebo response via better control over regression to the mean (RTM). Here, RTM represents an initial observed seizure rate higher than the long-term average, which gradually settles closer to the average, resulting in apparent response to treatment. This study used simulation to clarify the relationship between eligibility criteria and RTM. METHODS: Using a statistically realistic seizure diary simulator, the impact of RTM on placebo response and trial efficacy was explored by varying eligibility criteria for a traditional treatment phase II/III RCT for drug-resistant epilepsy. RESULTS: When the baseline period was included in the eligibility criteria, increasingly larger fractions of RTM were observed (25%-47% vs. 23%-25%). Higher fractions of RTM corresponded with higher expected placebo responses (50% responder rate [RR50]: 2%-9% vs. 0%-8%) and lower statistical efficacy (RR50: 47%-67% vs. 47%-81%). The exclusion of baseline from eligibility criteria was shown to decrease the number of patients needed by roughly 30%. SIGNIFICANCE: The manipulation of eligibility criteria for RCTs has a predictable and important impact on RTM, and therefore on placebo response; the difference between drug and placebo was more easily detected. This in turn impacts trial efficacy and therefore cost. This study found dramatic improvements in efficacy and cost when baseline was not included in eligibility.

Electroencephalographic Abnormalities are Common in COVID-19 and are Associated with Outcomes.

OBJECTIVE: The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. METHODS: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. RESULTS: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio [HR] 4.07 [1.44-11.51], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). INTERPRETATION: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes. ANN NEUROL 2021;89:872-883.

Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

OBJECTIVE: There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries. METHODS: Data from 5,419 patients on SeizureTracker.com (including seizure count, type, and duration) were split into training (3,806 patients/1,665,215 patient-days) and testing (1,613 patients/549,588 patient-days) sets with no overlapping patients. An artificial intelligence (AI) program, consisting of recurrent networks followed by a multilayer perceptron ("deep learning" model), was trained to produce risk forecasts. Forecasts were made from a sliding window of 3-month diary history for each day of each patient's diary. After training, the model parameters were held constant and the testing set was scored. A rate-matched random (RMR) forecast was compared to the AI. Comparisons were made using the area under the receiver operating characteristic curve (AUC), a measure of binary discrimination performance, and the Brier score, a measure of forecast calibration. The Brier skill score (BSS) measured the improvement of the AI Brier score compared to the benchmark RMR Brier score. Confidence intervals (CIs) on performance statistics were obtained via bootstrapping. RESULTS: The AUC was 0.86 (95% CI = 0.85-0.88) for AI and 0.83 (95% CI = 0.81-0.85) for RMR, favoring AI (p < 0.001). Overall (all patients combined), BSS was 0.27 (95% CI = 0.23-0.31), also favoring AI (p < 0.001). INTERPRETATION: The AI produced a valid forecast superior to a chance forecaster, and provided meaningful forecasts in the majority of patients. Future studies will be needed to quantify the clinical value of these forecasts for patients. ANN NEUROL 2020;88:588-595.

Statistical efficiency of patient data in randomized clinical trials of epilepsy treatments.

OBJECTIVE: Previous research suggests that natural fluctuations in seizure rates within individuals have a quantifiable impact on therapeutic clinical trial outcomes. METHODS: A trial simulator estimated the statistical power of clinical trials with a typical trial design with and without patients included who exhibited a range of means (1-15 seizures/mo) and standard deviations (1-15 seizures/mo) in their baseline seizure rates. Trial outcomes were evaluated using 50% responder rates, median percentage change, and time to prerandomization. RESULTS: Patients with higher seizure frequencies and lower standard deviations during their baseline contribute more to the statistical power regardless of the method used to evaluate the trial. Power varied from -20% to 30% depending on baseline seizure characteristics. SIGNIFICANCE: Patient-specific characteristics can predict the contributions to the statistical power of clinical trials for epilepsy treatments. It may be possible to characterize this contribution with baseline data, leading to more efficient clinical trials.

Prospective validation study of an epilepsy seizure risk system for outpatient evaluation.

OBJECTIVE: We conducted clinical testing of an automated Bayesian machine learning algorithm (Epilepsy Seizure Assessment Tool [EpiSAT]) for outpatient seizure risk assessment using seizure counting data, and validated performance against specialized epilepsy clinician experts. METHODS: We conducted a prospective longitudinal study of EpiSAT performance against 24 specialized clinician experts at three tertiary referral epilepsy centers in the United States. Accuracy, interrater reliability, and intra-rater reliability of EpiSAT for correctly identifying changes in seizure risk (improvements, worsening, or no change) were evaluated using 120 seizures from four synthetic seizure diaries (seizure risk known) and 120 seizures from four real seizure diaries (seizure risk unknown). The proportion of observed agreement between EpiSAT and clinicians was evaluated to assess compatibility of EpiSAT with clinical decision patterns by epilepsy experts. RESULTS: EpiSAT exhibited substantial observed agreement (75.4%) with clinicians for assessing seizure risk. The mean accuracy of epilepsy providers for correctly assessing seizure risk was 74.7%. EpiSAT accurately identified seizure risk in 87.5% of seizure diary entries, corresponding to a significant improvement of 17.4% (P = .002). Clinicians exhibited low-to-moderate interrater reliability for seizure risk assessment (Krippendorff's α = 0.46) with good intrarater reliability across a 4- to 12-week evaluation period (Scott's π = 0.89). SIGNIFICANCE: These results validate the ability of EpiSAT to yield objective clinical recommendations on seizure risk which follow decision patterns similar to those from specialized epilepsy providers, but with improved accuracy and reproducibility. This algorithm may serve as a useful clinical decision support system for quantitative analysis of clinical seizure frequency in clinical epilepsy practice.

Machine learning applications in epilepsy.

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

Comparing the efficacy, exposure, and cost of clinical trial analysis methods.

This study aimed to compare three commonly used analysis methods for clinical trials in epilepsy in terms of statistical efficiency, nonefficacious exposure, and cost. A realistic seizure diary simulator was employed to produce 102 000 trials, which were analyzed by the 50%-responder rate method (RR50), median percentage change (MPC), and time to prerandomization (TTP). Half the trials compared a placebo to a drug that was 20% better, and the other half compared two placebos. The former were used to calculate statistical power; the latter were used for type 1 error rates. Based on the number of patients needed to achieve 90% power, expected number of patient-days of nonefficacious exposures and expected cost were calculated for each method. MPC demonstrated the highest efficacy, lowest exposure, and lowest cost. RR50 demonstrated the lowest efficacy, highest exposure, and highest cost. Costs were: MPC $1 295 000, TTP $1 315 720, and RR50 $2 331 000. Selecting an optimal analysis method for a primary outcome in an epilepsy trial can have consequences in terms of nonefficacious exposure and cost. This study provides evidence supporting the use of MPC (preferred) or TTP, and evidence suggesting that RR50 would incur high costs and excess exposures.

Daylight saving time transitions are not associated with increased seizure incidence.

OBJECTIVE: Given the known association of daylight saving time (DST) transitions with increased risk of accidents, heart attack, and stroke, we aimed to determine whether seizures, which are reportedly influenced by sleep and circadian disruption, also increased in frequency following the transition into DST. METHODS: Using Seizure Tracker's self-reported data from 12 401 individuals from 2008-2016, 932 717 seizures were assessed for changes in incidence in relation to DST transitions. Two methods of standardization-z scores and unit-scaled rate ratios (RRs)-were used to compare seizure propensities following DST transitions to other time periods. RESULTS: As a percentile relative to all other weeks in a given year, absolute seizure counts in the week of DST fell below the median (DST seizure percentiles mean ± SD: 19.68 ± 16.25, P = 0.01), which was concordant with weekday-specific comparisons. Comparatively, RRs for whole-week (1.06, 95% confidence interval [CI] 1.02-1.10, P = 0.0054) and weekday-to-weekday (RR range 1.04-1.16, all P < 0.001) comparisons suggested a slightly higher incidence of seizures in the DST week compared to all other weeks of the year. However, examining the similar risk of the week preceding and following the DST-transition week revealed no significant weekday-to-weekday differences in seizure incidence, although there was an unexpected, modestly decreased seizure propensity in the DST week relative to the whole week prior (RR 0.94, 95% CI 0.91-0.96, P < 0.001). SIGNIFICANCE: Despite expectations that circadian and sleep disruption related to DST transitions would increase the incidence of seizures, we found little substantive evidence for such an association in this large, longitudinal cohort. Although large-scale observational/epidemiologic cohorts can be effective at answering such questions, additional covariates (eg, sleep duration, seizure type, and so on) that may underpin the association were not able available, so the association has not definitively been ruled out.

When can we trust responders? Serious concerns when using 50% response rate to assess clinical trials.

Individual seizure rates are highly volatile, with large fluctuations from month-to-month. Nevertheless, changes in individual mean seizure rates are used to measure whether or not trial participants successfully respond to treatment. This study aims to quantify the challenges in identifying individual treatment responders in epilepsy. A power calculation was performed to determine the trial duration required to detect a significant 50% decrease in seizure rates (P < .05) for individuals. Seizure rate simulations were also performed to determine the number of people who would appear to be 50% responders by chance. Seizure rate statistics were derived from long-term seizure counts recorded during a previous clinical trial for an implantable seizure monitoring device. We showed that individual variance in monthly seizure rates can lead to an unacceptably high false-positive rate in the detection of individual treatment responders. This error rate cannot be reduced by increasing the trial population; however, it can be reduced by increasing the duration of clinical trials. This finding suggests that some drugs may be incorrectly evaluated as effective; or, conversely, that helpful drugs could be rejected based on 50% response rates. It is important to pursue more nuanced approaches to measuring individual treatment response, which consider the patient-specific distributions of seizure rates.

Are the days of counting seizures numbered?

PURPOSE OF REVIEW: The estimation of seizure frequency is a cornerstone of clinical management of epilepsy and the evaluation of new therapies. Current estimation approaches are significantly limited by several factors. Comparing patient diaries and objective estimates (through both inpatient video-EEG monitoring of and long-term ambulatory EEG studies) reveal that patients document seizures inaccurately. So far, few practical alternative methods of estimation have been available. RECENT FINDINGS: We review the systems of counting currently utilized and their limitations, as well as the limitations imposed by problems defining clinical events. Alternative methodologies that permit the volatility of seizure rates to be accommodated, and possible alternative measures of brain excitability will be outlined. Recent developments in technologies around data capture, such as wearable and implantable devices, as well as significant advances in the ability to analyse the large data-sets supplied by these systems have provided a wealth of information. SUMMARY: There are now unprecedented opportunities to utilize and apply these insights in routine clinical management and assessment of therapies. The rapid adoption of long-term, wearable monitoring systems will permit major advances in our understanding of the natural history of epilepsy, and lead to more effective therapies and improved patient safety.

Different as night and day: Patterns of isolated seizures, clusters, and status epilepticus.

Using approximations based on presumed U.S. time zones, we characterized day and nighttime seizure patterns in a patient-reported database, Seizure Tracker. A total of 632 995 seizures (9698 patients) were classified into 4 categories: isolated seizure event (ISE), cluster without status epilepticus (CWOS), cluster including status epilepticus (CIS), and status epilepticus (SE). We used a multinomial mixed-effects logistic regression model to calculate odds ratios (ORs) to determine night/day ratios for the difference between seizure patterns: ISE versus SE, ISE versus CWOS, ISE versus CIS, and CWOS versus CIS. Ranges of OR values were reported across cluster definitions. In adults, ISE was more likely at night compared to CWOS (OR = 1.49, 95% adjusted confidence interval [CI] = 1.36-1.63) and to CIS (OR = 1.61, 95% adjusted CI = 1.34-1.88). The ORs for ISE versus SE and CWOS versus SE were not significantly different regardless of cluster definition. In children, ISE was less likely at night compared to SE (OR = 0.85, 95% adjusted CI = 0.79-0.91). ISE was more likely at night compared to CWOS (OR = 1.35, 95% adjusted CI = 1.26-1.44) and CIS (OR = 1.65, 95% adjusted CI = 1.44-1.86). CWOS was more likely during the night compared to CIS (OR = 1.22, 95% adjusted CI = 1.05-1.39). With the exception of SE in children, our data suggest that more severe patterns favor daytime. This suggests distinct day/night preferences for different seizure patterns in children and adults.

Common data elements for epilepsy mobile health systems.

OBJECTIVE: Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data. METHODS: Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems. RESULTS: A consensus statement is presented for epilepsy mHealth CDEs. SIGNIFICANCE: Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.

Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy.

OBJECTIVE: Sudden unexplained death in epilepsy (SUDEP) during inpatient electroencephalography (EEG) monitoring has been a rare but potentially preventable event, with associated cardiopulmonary markers. To date, no systematic evaluation of alarm settings for a continuous pulse oximeter (SpO2 ) has been performed. In addition, evaluation of the interrelationship between the ictal and interictal states for cardiopulmonary measures has not been reported. METHODS: Patients with epilepsy were monitored using video-EEG, SpO2 , and electrocardiography (ECG). Alarm thresholds were tested systematically, balancing the number of false alarms with true seizure detections. Additional cardiopulmonary patterns were explored using automated ECG analysis software. RESULTS: One hundred ninety-three seizures (32 generalized) were evaluated from 45 patients (7,104 h recorded). Alarm thresholds of 80-86% SpO2 detected 63-73% of all generalized convulsions and 20-28% of all focal seizures (81-94% of generalized and 25-36% of focal seizures when considering only evaluable data). These same thresholds resulted in 25-146 min between false alarms. The sequential probability of ictal SpO2 revealed a potential common seizure termination pathway of desaturation. A statistical model of corrected QT intervals (QTc), heart rate (HR), and SpO2 revealed close cardiopulmonary coupling ictally. Joint probability maps of QTc and SpO2 demonstrated that many patients had baseline dysfunction in either cardiac, pulmonary, or both domains, and that ictally there was dissociation-some patients exhibited further dysfunction in one or both domains. SIGNIFICANCE: Optimal selection of continuous pulse oximetry thresholds involves a tradeoff between seizure detection accuracy and false alarm frequency. Alarming at 86% for patients that tend to have fewer false alarms and at 80% for those who have more, would likely result in a reasonable tradeoff. The cardiopulmonary findings may lead to SUDEP biomarkers and early seizure termination therapies.

A big data approach to the development of mixed-effects models for seizure count data.

OBJECTIVE: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS: For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE: The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.