Eleven patients being evaluated with intracranial electroencephalography for medically resistant temporal lobe epilepsy participated in a visual recognition memory task. Interictal epileptiform ...spikes were manually marked and their rate of occurrence compared between baseline and three 2 s periods spanning a 6 s viewing period. During successful, but not unsuccessful, encoding of the images there was a significant reduction in interictal epileptiform spike rate in the amygdala, hippocampus, and temporal cortex. During the earliest encoding period (0-2000 ms after image presentation) in these trials there was a widespread decrease in the power of theta, alpha and beta band local field potential oscillations that coincided with emergent focal gamma frequency activity. Interictal epileptiform spike rate correlated with spectral band power changes and broadband (4-150 Hz) desynchronization, which predicted significant reduction in interictal epileptiform spike rate. Spike-triggered averaging of the field potential power spectrum detected a burst of low frequency synchronization 200 ms before the interictal epileptiform spikes that arose during this period of encoding. We conclude that interictal epileptiform spikes are modulated by the patterns of network oscillatory activity that accompany human memory offering a new mechanistic insight into the interplay of cognitive processing, local field potential dynamics and interictal epileptiform spike generation.
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.
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.
. The detection of seizures using wearable devices would improve epilepsy management, but reliable detection of seizures in an ambulatory environment remains challenging, and current studies lack ...concurrent validation of seizures using electroencephalography (EEG) data.
. An adaptively trained long-short-term memory deep neural network was developed and trained using a modest number of seizure data sets from wrist-worn devices. Transfer learning was used to adapt a classifier that was initially trained on intracranial electroencephalography (iEEG) signals to facilitate classification of non-EEG physiological datasets comprising accelerometry, blood volume pulse, skin electrodermal activity, heart rate, and temperature signals. The algorithm's performance was assessed with and without pre-training on iEEG signals and transfer learning. To assess the performance of the seizure detection classifier using long-term ambulatory data, wearable devices were used for multiple months with an implanted neurostimulator capable of recording iEEG signals, which provided independent electrographic seizure detections that were reviewed by a board-certified epileptologist.
. For 19 motor seizures from 10 in-hospital patients, the algorithm yielded a mean area under curve (AUC), a sensitivity, and an false alarm rate per day (FAR/day) of 0.98, 0.93, and 2.3, respectively. Additionally, for eight seizures with probable motor semiology from two ambulatory patients, the classifier achieved a mean AUC of 0.97 and an FAR of 2.45 events/day at a sensitivity of 0.9. For all seizure types in the ambulatory setting, the classifier had a mean AUC of 0.82 with a sensitivity of 0.47 and an FAR of 7.2 events/day.
. The performance of the algorithm was evaluated using motor and non-motor seizures during in-hospital and ambulatory use. The classifier was able to detect multiple types of motor and non-motor seizures, but performed significantly better on motor seizures.
The objective of this study was to describe the sEEG-defined seizure onset zone (SOZ), seizure semiology, presurgical evaluations, surgical intervention and outcome in patients with midline onset ...noninvasive phase I monitoring.
A single center sEEG database was reviewed to identify patients with seizures onset predominantly involving midline electrodes (FZ, CZ, PZ, OZ) on scalp EEG. Data abstracted included clinical factors, seizure semiology graded into lobar segmentation, imaging and electrographic findings, sEEG plan, interventions, and outcome.
Twelve patients were identified (8 males, median age of sEEG 28 years) out of 100 cases of sEEG performed from January 2015-September 2019. “Frontal lobe” seizure semiology was the most common. sEEG-defined SOZ were frontal (5), diffuse (1), multifocal (1), frontal and insular (1), frontal and cingulate (1), insular (1), cingulate (1), and mesial temporal (1). CZ and/or FZ scalp EEG changes were present for all patients with SOZ involving the frontal, cingulate, and insular regions. PZ/OZ scalp involvement was present in one patient with mesial temporal SOZ. Four patients underwent a definitive resective or ablative surgery, and the remaining patients underwent a palliative intervention. Of those with follow-up information available, 8/11 had seizure reduction by ≥ 50%, including 4 with an Engel I outcome. No clinical factors were associated with outcome.
SOZ for midline onset seizures from noninvasive phase I monitoring was most commonly in the frontal, cingulate, and insular regions. A complex cortical network between these regions may explain overlap in semiology and scalp EEG findings. While the number rendered seizure-free was limited, a significant proportion experienced a reasonably favorable outcome justifying use of sEEG to identify surgical options in these patients.
•Midline onset seizures on scalp EEG are rare.•sEEG commonly shows frontal, insular, and/or cingulate onset for these seizures.•A complex seizure network is likely underlying this.
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.
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.
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.
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.
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.
Could direct electrical stimulation of the brain be used to enhance memory? In patients with epilepsy undergoing evaluation for resective surgery, Kucewiczet al. show that stimulation of the lateral ...temporal cortex, but not the hippocampus, parahippocampal neocortex or prefrontal cortex, increases the number of words that patients can remember.
Abstract
Direct electrical stimulation of the human brain can elicit sensory and motor perceptions as well as recall of memories. Stimulating higher order association areas of the lateral temporal cortex in particular was reported to activate visual and auditory memory representations of past experiences (Penfield and Perot, 1963). We hypothesized that this effect could be used to modulate memory processing. Recent attempts at memory enhancement in the human brain have been focused on the hippocampus and other mesial temporal lobe structures, with a few reports of memory improvement in small studies of individual brain regions. Here, we investigated the effect of stimulation in four brain regions known to support declarative memory: hippocampus, parahippocampal neocortex, prefrontal cortex and temporal cortex. Intracranial electrode recordings with stimulation were used to assess verbal memory performance in a group of 22 patients (nine males). We show enhanced performance with electrical stimulation in the lateral temporal cortex (paired t-test, P = 0.0067), but not in the other brain regions tested. This selective enhancement was observed both on the group level, and for two of the four individual subjects stimulated in the temporal cortex. This study shows that electrical stimulation in specific brain areas can enhance verbal memory performance in humans.
awx373media1
5704855796001
Sentinel epileptiform discharges (SEDs) are epileptiform transients preceding the onset of a focal seizure seen on scalp EEG. Despite their potential localizing value, formal study of SED has been ...limited. The authors report a patient with MRI-negative focal-onset epilepsy whose seizures on scalp and intracranial EEG were always preceded by SED. Although the location and morphology of the SED was invariable, the seizures after the discharge were of two clearly distinct types, each with different semiology and region of spread on intracranial EEG. This suggests that the SED played a role in activating two distinct seizure networks. A right temporal lobectomy with amygdalohippocampectomy was performed. The resection included both the region of the SED as well as the seizure-onset zone of the more common seizure type, achieving seizure freedom at 3 years after surgery. Further research exploring whether the localization of SED is a reliable indicator of the seizure-onset zone could aid surgical planning in patients whose seizures are preceded by SED.
PDF
