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.
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
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.
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.
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 (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 number of patients needed by roughly 30%.
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.
Are the days of counting seizures numbered? Karoly, Philippa; Goldenholz, Daniel M; Cook, Mark
Current opinion in neurology,
04/2018, Letnik:
31, Številka:
2
Journal Article
Recenzirano
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.
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.
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.
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.
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.
•Trial simulations utilize a model of seizure count data to produce simulated placebo.•The model is able to replicate placebo response as seen in 23 actual RCTs.•The model is parsimonious, ...facilitating extensions.
: 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.
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.
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).
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.
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.
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