Viana, Pedro F

Abstract Objective This study reports development and validation of a deep learning seizure detection algorithm for two‐channel subscalp electroencephalographic (EEG) recordings. Ultra‐long‐term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra‐long‐term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers. Methods The convolutional neural network‐bidirectional long short‐term memory (CNN‐BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded at three centers were available for algorithm development and testing. EEG was recorded for a median of 63 days (range = 9–508), and a median of 17 seizures (range = 0–96) were recorded. Training data were augmented with scalp EEG seizures, and performance was benchmarked against a conventional spectral power classifier algorithm. We also evaluated an intrapatient training–testing approach where the algorithm was trained on the first 45% of data. Results The CNN‐BiLSTM trained on scalp EEG and subscalp EEG achieved an area under the receiver operating characteristic curve (AUROC) of .98 and an area under the precision–recall curve (AUPRC) of .50, which correspond to 94% sensitivity with 1.11 false detections per day. The same model trained on iEEG achieved only AUROC = .94 and AUPRC = .36. The conventional spectral band power detector achieved AUROC = .93 and AUPRC = .38. The CNN‐BiLSTM detector trained on the earliest half of the subscalp EEG data achieved AUROC = .93 and AUPRC = .37, corresponding to 87% sensitivity and 5.9 false detections per day. Significance High sensitivity and specificity are possible in automated seizure detection in two‐channel subscalp EEG data using a CNN‐BiLSTM framework. Performance of the detector is superior using subcutaneous EEG data for training rather than intracranial EEG, but addition of scalp EEG seizures for training was helpful.

Abstract Objective Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near‐continuous cerebral monitoring in real‐world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these systems remain unclear. We report on the longest observational study using ultra‐long‐term sqEEG to date. Methods We conducted a 15‐month prospective, observational study including 10 adult people with treatment‐resistant epilepsy. After device implantation, patients were asked to record sqEEG, to use an electronic seizure diary, and to complete acceptability and usability questionnaires. sqEEG seizures were annotated visually, aided by automated detection. Individualized temporal patterns of seizure occurrence were assessed via circadian circular statistics and via Fano factor analysis. Results Over a median duration of 438 days, 10 patients recorded a median 18.8 h/day, totaling 71 984 h of real‐world sqEEG data. Adherence and acceptability remained high throughout the study. Although 754 sqEEG seizures were recorded across patients, more than half (52%) of these were not reported in the patient diary. Of the 140 (27%) diary reports not associated with an identifiable sqEEG seizure, the majority (68%) were reported as seizures with preserved awareness. The sqEEG to diary F1 agreement score was highly variable, ranging from .06 to .97. Patient‐specific patterns of circadian seizure occurrence and seizure clustering were found, including several relevant discrepancies between sqEEG and diary. Significance We demonstrate feasibility and high acceptability of ultra‐long‐term (months–years) sqEEG monitoring. These systems help provide real‐world, more objective seizure counting compared to patient diaries. It is possible to objectively monitor individual temporal fluctuations of seizure occurrence.

AbstractObjectiveEpilepsy 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.MethodsUsing 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.ResultsThe 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.SignificanceThe 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.

Abstract Objective Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self‐reported and electroencephalographically (EEG)‐confirmed seizures in ambulatory patients with epilepsy in their natural home environments. Methods Long‐term e‐surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e‐surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed‐effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results Fifty‐four subjects returned 10 269 e‐survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12–3.61, AUC = .61, p = .02) was associated with increased relative odds of future self‐reported seizures. Multivariate analysis showed that previous self‐reported seizures (OR = 5.37, 95% CI = 3.53–8.16, AUC = .76, p < .001) were most strongly associated with future self‐reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87–5.95, AUC = .69, p < .001) remained significant when prior self‐reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e‐survey responses and subsequent EEG seizures. Significance Our results suggest that patients may tend to self‐forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self‐predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.

Abstract Objective The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. Methods In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. Results Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. Significance Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.

Abstract This study describes a generalized cross‐patient seizure‐forecasting approach using recurrent neural networks with ultra‐long‐term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short‐term memory (LSTM) deep‐learning classifiers. Electrographic seizures were identified by a board‐certified epileptologist. One‐minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise‐added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo‐prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave‐one‐patient‐out cross‐validation method, and the area under the receiver‐operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave‐one‐signal‐out method with repeated training and testing for each classifier. Cross‐patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution ( p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross‐patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two‐channel ultra‐long‐term sqEEG.

Abstract Objective One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient‐specific seizure forecasting is possible using remote, minimally invasive ultra‐long‐term subcutaneous EEG. Methods We analyzed a two‐center cohort of ultra‐long‐term subcutaneous EEG recordings, including six patients with drug‐resistant focal epilepsy monitored for 46–230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject‐specific long short‐term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65–.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient‐specific circadian patterns of seizure occurrence. Significance This study demonstrates proof‐of‐principle for the possibility of subject‐specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra‐long‐term at‐home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.

Abstract Objective Ultra long‐term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long‐term quality and consistency of the sqEEG signal, which is the objective of this study. Methods The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG ™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long‐term temporal trends in signal impedance and power spectral features were investigated with subject‐specific linear regression models and group‐level linear mixed‐effects models. Results sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8‐13Hz) and nocturnal peaks in the sigma range (12‐16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. Significance The spectral characteristics of minimally invasive, ultra long‐term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain‐computer interfaces.

AbstractWe describe the longest period of subcutaneous EEG (sqEEG) monitoring to date, in a 35‐year‐old female with refractory epilepsy. Over 230 days, 4791/5520 h of sqEEG were recorded (86%, mean 20.8 [IQR 3.9] hours/day). Using an electronic diary, the patient reported 22 seizures, while automatically‐assisted visual sqEEG review detected 32 seizures. There was substantial agreement between days of reported and recorded seizures (Cohen’s kappa 0.664), although multiple clustered seizures remained undocumented. Circular statistics identified significant sqEEG seizure cycles at circadian (24‐hour) and multidien (5‐day) timescales. Electrographic seizure monitoring and analysis of long‐term seizure cycles are possible with this neurophysiological tool.

Abstract Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.