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Objective: This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic-clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts., Methods: We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates., Results: Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90-1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08-26); (2) the linear relationship (slope = .79, R 2  = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%)., Significance: Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

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., (© 2024 International League Against Epilepsy.)

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., (© 2024 International League Against 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., (© 2023 International League Against Epilepsy.)

Purpose: This study investigated the characteristics of patients presenting with the first-time seizure (FTS) and whether neurology follow-up occurred in a medically underserved area., Methods: A retrospective study of adults with a FTS discharged from the Emergency Department (ED) at Loma Linda University between January 1, 2017 and December 31, 2018 was performed. The primary outcome was days from the ED visit to the first neurology visit. Secondary outcomes included repeat ED visits, percentage of patients who had specialty assessment in one year, type of neurologist seen, and percentage lost to follow-up., Results: Of the 1327 patients screened, 753 encounters met criteria for manual review, and after exclusion criteria were applied, 66 unique encounters were eligible. Only 30% of FTS patients followed up with a neurologist. The median duration for neurology follow-up was 92 days (range=5-1180). After initial ED visit, 20% of follow-up patients were diagnosed with epilepsy within 189 days, and 20% of patients re-presented to the ED with recurrent seizures while awaiting their initial neurology appointment. Reasons for lack of follow-up included: referral issues, missed appointments, and shortage of available neurologists., Conclusion: This study highlights the significant treatment gap that a first-time seizure clinic (FTSC) could fill in underserved communities. FTSC may reduce the morbidity and mortality associated with untreated recurrent seizures., Competing Interests: Declaration of Competing Interest D. Goldenholz – is a scientific advisor for Magic Leap, Epilepsy AI and Eysz. He has funding from NIH NINDS K23NS124656. A. Cabrera, C. Jenkins, N. Baker, T. Losey, M. Estes, C. Casassa – Declaration of interest: None., (Copyright © 2023. Published by Elsevier Ltd.)

Wearable devices have made it easier to generate and share data collected on individuals. This systematic review seeks to investigate whether deidentifying data from wearable devices is sufficient to protect the privacy of individuals in datasets. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on Dec 6, 2021 (PROSPERO registration number CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Although our search strategy had no language restrictions, all retrieved studies were in English. We included studies showing reidentification, identification, or authentication with data from wearable devices. Our search retrieved 17 625 studies, and 72 studies met our inclusion criteria. We designed a custom assessment tool for study quality and risk of bias assessments. 64 studies were classified as high quality and eight as moderate quality, and we did not detect any bias in any of the included studies. Correct identification rates were typically 86-100%, indicating a high risk of reidentification. Additionally, as little as 1-300 s of recording were required to enable reidentification from sensors that are generally not thought to generate identifiable information, such as electrocardiograms. These findings call for concerted efforts to rethink methods for data sharing to promote advances in research innovation while preventing the loss of individual privacy., Competing Interests: Declaration of interests JD is a scientific adviser with stock options at Veri; she received a grant from the Chan Zuckerberg Initiative, consulting fees from Gold Track, honoraria from the American College of Cardiology and Shionogi, and support for attending meetings (ICAMPAM 2022 and PMWC 2022). DMG is a scientific adviser for Magic Leap and Epilepsy AI; he has stock options with Magic Leap and has received gifts and services from Magic Leap. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Before integrating new machine learning (ML) into clinical practice, algorithms must undergo validation. Validation studies require sample size estimates. Unlike hypothesis testing studies seeking a p -value, the goal of validating predictive models is obtaining estimates of model performance. There is no standard tool for determining sample size estimates for clinical validation studies for machine learning models., Methods: Our open-source method, Sample Size Analysis for Machine Learning (SSAML) was described and was tested in three previously published models: brain age to predict mortality (Cox Proportional Hazard), COVID hospitalization risk prediction (ordinal regression), and seizure risk forecasting (deep learning)., Results: Minimum sample sizes were obtained in each dataset using standardized criteria., Discussion: SSAML provides a formal expectation of precision and accuracy at a desired confidence level. SSAML is open-source and agnostic to data type and ML model. It can be used for clinical validation studies of ML models.

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., (© 2022 International League Against Epilepsy.)

People with epilepsy can experience tremendous stress from the uncertainty of when a seizure will occur. Three factors deemed important because of their potential influence on seizure risk are exercise, medication adherence, and the menstrual cycle. A narrative review was conducted through PubMed searching for relevant articles on how seizure risk is modified by 1) exercise, 2) medication adherence, and 3) the menstrual cycle. There was no consensus about the impact of exercise on seizure risk. Studies about medication nonadherence suggested an increase in seizure risk, but there was not a sufficient amount of data for a definitive conclusion. Most studies about the menstrual cycle reported an increase in seizures connected to a specific aspect of the menstrual cycle. No definitive studies were available to quantify this impact precisely. All three triggers reviewed had gaps in the research available, making it not yet possible to definitively quantify a relationship to seizure risk. More quantitative prospective studies are needed to ascertain the extent to which these triggers modify seizure risk., Competing Interests: Declarations of interest None., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Importance: Epilepsy affects at least 1.2% of the population, with one-third of cases considered to be drug-resistant epilepsy (DRE). For these cases, focal cooling therapy may be a potential avenue for treatment, offering hope to people with DRE for freedom from seizure. The therapy leverages neuroscience and engineering principles to deliver a reversible treatment unhindered by pharmacology., Observations: Analogous to (but safer than) the use of global cooling in postcardiac arrest and neonatal ischemic injury, extensive research supports the premise that focal cooling as a long-term treatment for epilepsy could be effective. The potential advantages of focal cooling are trifold: stopping epileptiform discharges, seizures, and status epilepticus safely across species (including humans)., Conclusions and Relevance: This Review presents the most current evidence supporting focal cooling in epilepsy. Cooling has been demonstrated as a potentially safe and effective treatment modality for DRE, although it is not yet ready for use in humans outside of randomized clinical trials. The Review will also offer a brief overview of the technical challenges related to focal cooling in humans, including the optimal device design and cooling parameters.

Purpose: Recently a realistic simulator of patient seizure diaries was developed that can reproduce effects seen in randomized clinical trials (RCTs). RCTs suffer from high costs and statistical inefficiencies. Using realistic simulation and machine learning this study aimed to identify a more statistically efficient outcome metric., Methods: Five candidate deep learning architectures with 54 permutations of hyperparameters were compared to the traditional standard, median percent change (MPC). Each were also tested for type 1 error. All models had similar outcomes, with appropriate low levels of type 1 error., Results: The simplest model was equivalent to a logistic regression of a histogram of individual percentage changes in seizure rate, requiring 21-22% less patients to discriminate drug from placebo at 90% power. This model was referred to as LPC., Conclusion: Future studies to validate LPC may enable faster, cheaper and more efficient clinical trials., (Copyright © 2021 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Preemptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of preempting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm., (© 2021 American Academy of Neurology.)

Background: Use of electronic seizure diaries (e-diaries) by caregivers of children with epileptic spasms is not well understood. We describe the demographic and seizure-related information of children with epileptic spasms captured in a widely used e-diary and explore the potential biases in how caregivers report these data., Methods: We analyzed children with epileptic spasms in an e-diary, Seizure Tracker, from 2007 to 2018. We described variables including sex, time of seizure, percentage of spasms occurring as individual spasms (versus in clusters), cluster duration, and number of spasms per cluster. We compared seizure characteristics in the e-diary cohort with published cohorts to identify biases in caregiver-reported epileptic spasms. We also reviewed seizure patterns in a small cohort of children with epileptic spasms monitored on overnight video-electroencephalography (vEEG)., Results: There were 314 children in the e-diary cohort and nine children in the vEEG cohort. The e-diary cohort was more likely than expected to report counts divisible by five. The e-diary cohort had a lower proportion of nighttime spasms than expected based on data from published cohorts. The e-diary cohort had a significantly lower percentage of spasms as individual spasms, a greater number of spasms per cluster, and a greater cluster duration relative to the vEEG cohort., Conclusions: Caregivers using e-diaries for epileptic spasms may miss individual spams, be more likely to report long clusters, round counts to the nearest five, and underreport nighttime spasms. Clinicians should be aware of these reporting biases when using e-diary data to guide care for children with epileptic spasms., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (© 2021 American Neurological Association.)

Objective: This study sought to understand issues facing people with epilepsy (PWE) during the lockdown period of the COVID-19 pandemic in the United States., Methods: We conducted a cross-sectional study using a 20-question survey that used SeziureTracker.com, sent to eligible PWE and their caregivers on May 6th, 2020. Questions about demographics and medical history were used to calculate COVID mortality risk odds ratios (OR) compared to a low baseline risk group., Results: In total, 505 responses were collected. Of these, 71% reported no change in seizure rates and 25% reported an increase in seizures, which they attributed primarily to disrupted sleep (63%) and decreased exercise (42%). Mortality risks from COVID-19 had median OR of 1.67, ranging 1.00-906.98. Fear about hospitalization (53%) and concern for loved ones (52%) were prominent concerns. Of the respondents, 5% reported stopping or reducing anti-seizure medications due to problems communicating with doctors, access or cost. Lower-risk COVID patients reported more fear of hospitalization (55% versus 38%, p<0.001) and anxiety about medication access (43% versus 28%, p=0.03) compared with higher-risk COVID patients. Increased anxiety was reported in 47%, and increased depression in 28%. Ten percent without generalized convulsions and 8% with did not know anything about epilepsy devices (VNS, RNS, DBS)., Significance: The COVID-19 pandemic presents unique challenges to PWE, including increased seizure rates, problems with access and cost of life-saving medications. Those with lower COVID-19 risk may have been marginalized more than those with higher risk. Efforts to protect PWE during major public health emergencies should take these findings into account.

Objective: There is a harmful myth that persists in modern culture that one should place objects into a seizing person's mouth to prevent "swallowing the tongue." Despite expert guidelines against this, the idea remains alive in popular media and public belief. We aimed to investigate the myth's origins and discredit it., Methods: A medical and popular literature review was conducted for the allusions to "swallowing one's tongue" and practice recommendations for and against placing objects into a seizing person's mouth. Current prevalence of these beliefs and relevant anatomy and physiology were summarised., Results: The first English language allusions to placing objects in a patient's mouth occurred in the mid-19th century, and the first allusions to swallowing one's tongue during a seizure occurred in the late 19th century. By the mid-20th century, it was clear that some were recommending against the practice of placing objects in a patient's mouth to prevent harm. Relatively recent popular literature and film continue to portray incorrect seizure first aid through at least 2013. There is ample modern literature confirming the anatomical impossibility of swallowing one's tongue and confirming the potential harm of putting objects in a patient's mouth., Conclusion: One cannot swallow their tongue during a seizure. Foreign objects should not be placed into a seizing person's mouth. We must continue to disseminate these ideas to our patients and colleagues. As neurologists, we have an obligation to champion safe practices for our patients, especially when popular media and culture continue to propagate dangerous ones., (Copyright © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

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., (© 2020 American Neurological Association.)

Objective: Seizure clusters are often encountered in people with poorly controlled epilepsy. Detection of seizure clusters is currently based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 h. Current definitions fail to distinguish between statistically significant clusters and those that may result from natural variation in the person's seizures. Ability to systematically define when a seizure cluster is significant for the individual carries major implications for treatment. However, there is no uniform consensus on how to define seizure clusters. This study proposes a principled statistical approach to defining seizure clusters that addresses these issues., Methods: A total of 533,968 clinical seizures from 1,748 people with epilepsy in the Seizure Tracker™ seizure diary database were used for algorithm development. We propose an algorithm for automated individualized seizure cluster identification combining cumulative sum change-point analysis with bootstrapping and aberration detection, which provides a new approach to personalized seizure cluster identification at user-specified levels of clinical significance. We develop a standalone user interface to make the proposed algorithm accessible for real-time seizure cluster identification (ClusterCalc™). Clinical impact of systematizing cluster identification is demonstrated by comparing empirically-defined clusters to those identified by routine seizure cluster definitions. We also demonstrate use of the Hurst exponent as a standardized measure of seizure clustering for comparison of seizure clustering burden within or across patients., Results: Seizure clustering was present in 26.7 % (95 % CI, 24.5-28.7 %) of people with epilepsy. Empirical tables were provided for standardizing inter- and intra-patient comparisons of seizure cluster tendency. Using the proposed algorithm, we found that 37.7-59.4 % of seizures identified as clusters based on routine definitions had high probability of occurring by chance. Several clusters identified by the algorithm were missed by conventional definitions. The utility of the ClusterCalc algorithm for individualized seizure cluster detection is demonstrated., Significance: This study proposes a principled statistical approach to individualized seizure cluster identification and demonstrates potential for real-time clinical usage through ClusterCalc. Using this approach accounts for individual variations in baseline seizure frequency and evaluates statistical significance. This new definition has the potential to improve individualized epilepsy treatment by systematizing identification of unrecognized seizure clusters and preventing unnecessary intervention for random events previously considered clusters., Competing Interests: Declaration of Competing Interest SC is the founder of EpilepsyAI, LLC. RM is the cofounder and owner of Seizure Tracker, LLC, cofounder of EpilepsyAI, LLC, and has received personal fees from Cyberonics, Courtagen, Engage Therapeutics, Brain Sentinel, Neurelis, UCB, and grants from the Tuberous Sclerosis Alliance. DG is on the medical advisory board of Magic Leap, and has received grant support from BIDMC and NIH T32NS048005. SH is on the Editorial Board of Epilepsy and Behavior. A patent application has been filed based on these results. The remaining authors have no conflicts of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Changes in patient-reported seizure frequencies are the gold standard used to test efficacy of new treatments in randomized controlled trials (RCTs). Recent analyses of patient seizure diary data suggest that the placebo response may be attributable to natural fluctuations in seizure frequency, though the evidence is incomplete. Here we develop a data-driven statistical model and assess the impact of the model on interpretation of placebo response., Methods: A synthetic seizure diary generator matching statistical properties seen across multiple epilepsy diary datasets was constructed. The model was used to simulate the placebo arm of 5000 RCTs. A meta-analysis of 23 historical RCTs was compared to the simulations., Results: The placebo 50 %-responder rate (RR50) was 27.3 ± 3.6 % (simulated) and 21.1 ± 10.0 % (historical). The placebo median percent change (MPC) was 22.0 ± 6.0 % (simulated) and 16.7 ± 10.3 % (historical)., Conclusions: A statistical model of daily seizure count generation which incorporates quantities related to the natural fluctuations of seizure count data produces a placebo response comparable to those seen in historical RCTs. This model may be useful in better understanding the seizure count fluctuations seen in patients in other clinical settings., Competing Interests: Declaration of Competing Interest The authors declare that there are no conflicts of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Placebos impact epilepsy in a number of ways. Through randomized clinical trials, explicit clinical use, and also through implicit clinical use, placebos play a role in epilepsy. This chapter will discuss the reasons placebo is used, the determinants of placebo response in epilepsy, observations about placebo specific to epilepsy, and ways in which clinical trial design is impacted by placebo., (© 2020 Elsevier Inc. All rights reserved.)

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., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

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., (Wiley Periodicals, Inc. © 2019 International League Against 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., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

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., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

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., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

Background: Epilepsy has long been suspected to be governed by cyclic rhythms, with seizure rates rising and falling periodically over weeks, months, or even years. The very long scales of seizure patterns seem to defy natural explanation and have sometimes been attributed to hormonal cycles or environmental factors. This study aimed to quantify the strength and prevalence of seizure cycles at multiple temporal scales across a large cohort of people with epilepsy., Methods: This retrospective cohort study used the two most comprehensive databases of human seizures (SeizureTracker [USA] and NeuroVista [Melbourne, VIC, Australia]) and analytic techniques from circular statistics to analyse patients with epilepsy for the presence and frequency of multitemporal cycles of seizure activity. NeuroVista patients were selected on the basis of having intractable focal epilepsy; data from patients with at least 30 clinical seizures were used. SeizureTracker participants are self selected and data do not adhere to any specific criteria; we used patients with a minimum of 100 seizures. The presence of seizure cycles over multiple time scales was measured using the mean resultant length (R value). The Rayleigh test and Hodges-Ajne test were used to test for circular uniformity. Monte-Carlo simulations were used to confirm the results of the Rayleigh test for seizure phase., Findings: We used data from 12 people from the NeuroVista study (data recorded from June 10, 2010, to Aug 22, 2012) and 1118 patients from the SeizureTracker database (data recorded from Jan 1, 2007, to Oct 19, 2015). At least 891 (80%) of 1118 patients in the SeizureTracker cohort and 11 (92%) of 12 patients in the NeuroVista cohort showed circadian (24 h) modulation of their seizure rates. In the NeuroVista cohort, patient 8 had a significant cycle at precisely 1 week. Two others (patients 1 and 7) also had approximately 1-week cycles. Patients 1 and 4 had 2-week cycles. In the SeizureTracker cohort, between 77 (7%) and 233 (21%) of the 1118 patients showed strong circaseptan (weekly) rhythms, with a clear 7-day period. Between 151 (14%) and 247 (22%) patients had significant seizure cycles that were longer than 3 weeks. Seizure cycles were equally prevalent in men and women, and peak seizure rates were evenly distributed across all days of the week., Interpretation: Our results suggest that seizure cycles are robust, patient specific, and more widespread than previously understood. They align with the accepted consensus that most epilepsies have some diurnal influence. Variations in seizure rate have important clinical implications. Detection and tracking of seizure cycles on a patient-specific basis should be standard in epilepsy management practices., Funding: Australian National Health and Medical Research Council., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Location data are becoming easier to obtain and are now bundled with other metadata in a variety of biomedical research applications. At the same time, the level of sophistication required to protect patient privacy is also increasing. In this article, we provide guidance for institutional review boards (IRBs) to make informed decisions about privacy protections in protocols involving location data. We provide an overview of some of the major categories of technical algorithms and medical-legal tools at the disposal of investigators, as well as the shortcomings of each. Although there is no "one size fits all" approach to privacy protection, this article attempts to describe a set of practical considerations that can be used by investigators, journal editors, and IRBs.

Objective: To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers., Methods: Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively., Results: A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p < 0.004)., Significance: This study explored a large cohort of patients with self-reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient-recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

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., (Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.)

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., (Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.)

Objective: A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk., Methods: Using data from SeizureTracker.com, a patient-reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed-effects hidden Markov model for zero-inflated count data was developed to estimate changes in underlying seizure risk using patient-reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com., Results: EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was <12 months, providing a data-driven estimate of the amount of time a person with TSC would be expected to remain at the same seizure risk level according to the natural course of epilepsy., Significance: We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient-reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (AED) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.

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.

The epilepsy surgery database from 1984 to 2012 at the National Institutes of Health (NIH) was reviewed to determine the association of postoperative electroencephalography (EEG) with seizure recurrence. Eighty-three patients were analyzed, with 41 having at least 5 years of follow-up. The relationship between epileptiform postoperative EEG findings and seizure recurrence at 1, 2, and 5 years was not significant, despite a significant decrease in abnormal EEG recordings after surgery. Clinicians use a variety of tools to predict seizure recurrence following epilepsy surgery to guide medication management and to modulate patient expectations. EEG is but one tool for assessing the likelihood of seizure recurrence following epilepsy surgery.

Background: There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting., Methods: Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker ( n = 3016), Human Epilepsy Project ( n = 93), and NeuroVista ( n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions., Results: A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation ( R 2 > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%., Conclusion: Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.

Purpose: Clinical epilepsy drug trials have been measuring increasingly high placebo response rates, up to 40%. This study was designed to examine the relationship between the natural variability in epilepsy, and the placebo response seen in trials. We tested the hypothesis that 'reversing' trial direction, with the baseline period as the treatment observation phase, would reveal effects of natural variability., Method: Clinical trial simulations were run with time running forward and in reverse. Data sources were: SeizureTracker.com (patient reported diaries), a randomized sham-controlled TMS trial, and chronically implanted intracranial EEG electrodes. Outcomes were 50%-responder rates (RR50) and median percentage change (MPC)., Results: The RR50 results showed evidence that temporal reversal does not prevent large responder rates across datasets. The MPC results negative in the TMS dataset, and positive in the other two., Conclusions: Typical RR50s of clinical trials can be reproduced using the natural variability of epilepsy as a substrate across multiple datasets. Therefore, the placebo response in epilepsy clinical trials may be attributable almost entirely to this variability, rather than the "placebo effect"., (Published by Elsevier Ltd.)

Objective: Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, Z V ., Methods: Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, Z V . Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the Z V method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05)., Results: Prediction accuracy across datasets was, Z V : 91-100%, RR50: 42-80%. Simulated RCT Z V analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm., Significance: Z V may increase the statistical power of an RCT relative to the traditional RR50., (Published by Elsevier B.V.)

Objective: It is currently unknown if knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials., Methods: Using data obtained from 10 patients with chronically implanted subdural electrodes over an average of 1 year, a Monte Carlo bootstrapping simulation study was performed to estimate the statistical power of running a clinical trial based on A) patient reported seizures with intracranial EEG (icEEG) confirmation, B) all patient reported events, or C) all icEEG confirmed seizures. A "drug" was modeled as having 10%, 20%, 30%, 40% and 50% efficacy in 1000 simulated trials each. Outcomes were represented as percentage of trials that achieved p<0.05 using Fisher Exact test for 50%-responder rates (RR50), and Wilcoxon Rank Sum test for median percentage change (MPC)., Results: At each simulated drug strength, the MPC method showed higher power than RR50. As drug strength increased, statistical power increased. For all cases except RR50 with drug of 10% efficacy, using patient reported events (with or without icEEG confirmation) was not as statistically powerful as using all available intracranially confirmed seizures (p<0.001)., Significance: This study demonstrated using simulation that additional accuracy in seizure detection using chronically implanted icEEG improves statistical power of clinical trials. Newer invasive and noninvasive seizure detection devices may have the potential to provide greater statistical efficiency, accelerate drug discovery and lower trial costs., Competing Interests: Conflicts of Interest None of the authors have any conflict of interest to report.