Cenobamate: real-world data from a retrospective multicenter study.

BACKGROUND: Clinical trials have shown that cenobamate (CNB) is an efficacious and safe anti-seizure medication (ASM) for drug-resistant focal epilepsy. Here, we analyzed one of the largest real-world cohorts, covering the entire spectrum of epilepsy syndromes, the efficacy and safety of CNB, and resulting changes in concomitant ASMs. METHODS: We conducted a retrospective observational study investigating CNB usage in two German tertiary referral centers between October 2020 and June 2023 with follow-up data up to 27 months of treatment. Our primary outcome was treatment response. Secondary outcomes comprised drug response after 12 and 18 months, seizure freedom rates, CNB dosage and retention, adverse drug reactions (ADRs), and changes in concomitant ASMs. RESULTS: 116 patients received CNB for at least two weeks. At 6 months, 98 patients were eligible for evaluation. Thereof 50% (49/98) were responders with no relevant change at 12 and 18 months. Seizure freedom was achieved in 18.4% (18/98) at 6 months, 16.7% (11/66), and 3.0% (1/33) at 12 and 18 months. The number of previous ASMs did not affect the seizure response rate. Overall, CNB was well-tolerated, however, in 7.7% (9/116), ADRs led to treatment discontinuation. The most frequent changes of concomitant ASMs included the discontinuation or reduction of sodium channel inhibitors, clobazam reduction, and perampanel discontinuation, while brivaracetam doses were usually left unchanged. CONCLUSIONS: CNB proved to be a highly effective and generally well-tolerated ASM in patients with severe drug-resistant epilepsy, comprising a broad array of epilepsy syndromes beyond focal epilepsy.

Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.

Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives.Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%.Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity.Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

Video-EEG-monitoring to guide antiseizure medication withdrawal.

BACKGROUND: Discontinuing anti-seizure medication (ASM) should be considered in persons with epilepsy with long-term seizure freedom. Clinicians should also pursue ASM withdrawal in persons with one-time seizures without increased recurrence risk and those with suspected non-epileptic events. However, ASM withdrawal is associated with the risk of recurring seizures. Monitored ASM withdrawal in an epilepsy monitoring unit (EMU) could help better evaluate the risk of seizure recurrence. Here, we investigate the practice of EMU-guided ASM withdrawal, assess its indications, and aim to determine positive and negative predictors for successful withdrawal. METHODS: We screened the medical records of all patients admitted to our EMU between November 1, 2019, and October 31, 2021, and included patients of at least 18 years admitted with the aim of permanent ASM withdrawal. We defined four groups of withdrawal indications: (1) long-term seizure freedom; (2) suspected non-epileptic events; (3) history of epileptic seizures but not fulfilling diagnostic criteria of epilepsy; and (4) seizure-freedom after epilepsy surgery. Successful withdrawal was defined according to the following criteria: no recoding of (sub)clinical seizure activity during VEM (groups 1, 2, and 3), patients did not meet the International League Against Epilepsy (ILAE) definition of epilepsy (groups 2 and 3) [14], and patients were discharged without ongoing ASM treatment (all groups). We also evaluated the prediction model by Lamberink et al. (LPM) for the risk of seizure recurrence in groups 1 and 3. RESULTS: 55/651 (8.6%) patients fulfilled the inclusion criteria. Withdrawal indications were distributed as follows; group 1: 2/55 (3.6%); group 2: 44/55 (80%); group 3: 9/55 (16,4%); group 4: 0/55. Overall, ASM withdrawal was successful in 90.9%. The sensitivity of the LPM for a 2-year 50% relapse risk threshold was 75%, the specificity 33.3%; for a 5-year relapse risk respectively 12.5% and 33.3%, suggesting that the model is not suitable for risk assessment in patients with one-time seizures or acute-symptomatic seizures, who constituted most of the evaluated patients. CONCLUSIONS: Our study suggests that EMU-guided ASM withdrawal could be a helpful tool to support clinical decision-making and improve patient safety. Prospective, randomized trials should further evaluate this method in the future.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons.

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

Spectral Fusion of Heartbeat and Accelerometer Data for Estimation of Breathing Rate in Wearable Patches.

Despite developments in wearable devices for detecting various bio-signals, continuous measurement of breathing rate (BR) remains a challenge. This work presents an early proof of concept that employs a wearable patch to estimate BR. We propose combining techniques for calculating BR from electrocardiogram (ECG) and accelerometer (ACC) signals, while applying decision rules based on signal-to-noise (SNR) to fuse the estimates for improved accuracy.

Dissociation between the behavioural and electrophysiological effects of the face and body composite illusions.

Several studies have reported similarities between perceptual processes underlying face and body perception, particularly emphasizing the importance of configural processes. Differences between the perception of faces and the perception of bodies were observed by means of a manipulation targeting a specific subtype of configural processing: the composite illusion. The composite face illusion describes the fact that two identical top halves of a face are perceived as being different if they are presented with different bottom parts. This effect disappears, if both halves are laterally shifted. Crucially, the effect of misalignment is not observed for bodies. This study aimed to further explore differences in the time course of face and body perception by using the composite effect. The present results replicated behavioural effects illustrating that misalignment affects the perception of faces but not bodies. Thus, face but not body perception relies on holistic processing. However, differences in the time course of the processing of both stimulus categories emerged at the N170 and P200. The pattern of the behavioural data seemed to be related to the P200. Thus, the present data indicate that holistic processes associated with the effect of misalignment might occur 200 ms after stimulus onset.

Impaired Autonomic Responses to Emotional Stimuli in Autoimmune Limbic Encephalitis.

Limbic encephalitis (LE) is an autoimmune-mediated disorder that affects structures of the limbic system, in particular, the amygdala. The amygdala constitutes a brain area substantial for processing of emotional, especially fear-related signals. The amygdala is also involved in neuroendocrine and autonomic functions, including skin conductance responses (SCRs) to emotionally arousing stimuli. This study investigates behavioral and autonomic responses to discrete emotion evoking and neutral film clips in a patient suffering from LE associated with contactin-associated protein-2 (CASPR2) antibodies as compared to a healthy control group. Results show a lack of SCRs in the patient while watching the film clips, with significant differences compared to healthy controls in the case of fear-inducing videos. There was no comparable impairment in behavioral data (emotion report, valence, and arousal ratings). The results point to a defective modulation of sympathetic responses during emotional stimulation in patients with LE, probably due to impaired functioning of the amygdala.