Parental experiences and perspectives on the value of seizure detection while caring for a child with epilepsy: a qualitative study

Introduction: Caring for a child with epilepsy has a significant impact on parental quality of life. Seizure unpredictability and complications, including sudden unexpected death in epilepsy (SUDEP), may cause high parental stress and increased anxiety. Nocturnal supervision with seizure detection devices may lower SUDEP risk and decrease parental burden of seizure monitoring, but little is known about their added value in family homes. Methods: We conducted semi-structured in-depth interviews with parents of children with refractory epilepsy participating in the PROMISE trial (NCT03909984) to explore the value of seizure detection in the daily care of their child. Children were aged 4-16 years, treated at a tertiary epilepsy center, had at least one nocturnal major motor seizure per week, and used a wearable seizure detection device (NightWatch) for two months at home. Data were analyzed using inductive thematic analysis. Results: Twenty three parents of nineteen children with refractory epilepsy were interviewed. All parents expressed their fear of missing a large seizure and the possible consequences of not intervening in time. Some parents felt the threat of child loss during every seizure, while others thought about it from time to time. The fear could fluctuate over time, mainly associated with fluctuations of seizure frequency. Most parents described how they developed a protective behavior, driven by this fear. The way parents handled the care of their child and experienced the burden of care influenced their perceptions on the added value of NightWatch. The experienced value of NightWatch depended on the amount of assurance it could offer to reduce their fear and the associated protective behavior as well as their resilience to handle the potential extra burden of care, due to false alarms or technical problems. Conclusion: Healthcare professionals and device companies should be aware of parental protective behavior and the high parental burden of care and develop tailored strategies to optimize seizure detection device care. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).Paroxysmal Cerebral Disorder

Automated video-based detection of nocturnal motor seizures in children

Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real-time video-based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video-sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3-17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%-100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0-0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior-related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real-time use.Paroxysmal Cerebral Disorder

Medial temporal lobe activity during semantic classification using a flexible fMRI design

Item does not contain fulltextn this study, we evaluated the use of a self-paced PORI design, to allow a flexible speed of responding with only four alternating stimulus blocks to minimize the influence of task switching on a group of young subjects. This was done in view of our intention to use such a design on groups of elderly and demented subjects in the near future. In addition, the hypothesis was tested that the medial temporal lobe is involved in semantic memory similar to episodic memory using a semantic retrieval task. In line with previous imaging studies that compared a semantic (living/nonliving) to a perceptual (alphabetically ascending/descending) classification condition, activity was seen in lateral temporal and inferior frontal regions, indicating the applicability of our design. Additional activity was seen in the right, and, at a slightly lower threshold, also in the left MTL, providing support for the involvement of the MTL in retrieval from semantic memory

Ictal autonomic changes as a tool for seizure detection: a systematic review

Paroxysmal Cerebral Disorder

Parahippocampal activation during successful recognition of words: A self-paced event-related fMRI study

Item does not contain fulltextIn this study, we investigated retrieval from verbal episodic memory using a self-paced event-related fMRI paradigm, similar to the designs typically used in behavioral studies of memory function. We tested the hypothesis that the medial temporal lobe (MTL) is involved in the actual recovery of verbal information (retrieval success) rather than in the attempt to retrieve information (retrieval attempt), To this end, we used a verbal recognition task, distinguishing correctly recognized words, correctly rejected words, and a low-level baseline condition, Directly contrasting correct recognition with correct rejection of words, we found activation in the left fusiform/parahippocampal gyrus, indicating that this region has a distinct role in the successful retrieval of verbal information, Furthermore, our results were in agreement with those of previous imaging studies that compared a fixed-paced verbal recognition task to a baseline condition, showing activation in bilateral inferior frontal cortex, left dorsolateral prefrontal cortex, left anterior insular cortex, and anterior cingulate, This demonstrates the applicability of a self-paced event-related design within imaging studies of memory function

Non-convulsive status epilepticus detection

Non-convulsive status epilepticus (NCSE) is an epileptic process, where electrographic seizure activity persists over 10 minutes without noticeable motor symptoms [1]. Long-term NCSE with high degree of unresponsiveness may result in structural brain damage for the ICU patients. During the patients’ hospital stay, it is practically difficult to constantly make precise diagnosis of NCSE by clinicians via a routine procedure. Given the subtle and variable clinical symptoms, clinicians widely use electroencephalography (EEG) to diagnose NCSE. The ictal discharges during NCSE are visually analyzed by the clinicians based on some common morphological EEG patterns. However, the visual inspection by humans is time-consuming and subjective. Moreover, the safety of the chronic patients with NCSE is not guaranteed without proper monitoring. Daily monitoring of these patients unduly burdens their caregivers. Therefore, a 24/7 automatic NCSE detection system via continuous EEG signals is desirable at both hospital and home. We aim to develop a ‘brainwave’ chip, which can constantly monitor the EEG signals from NCSE patients. An automatic NCSE detection algorithm applied on this chip is investigated. This is a retrospective observational study with existing EEG and one-lead ECG recordings from two groups: 16 participants with a clinical diagnosis of NCSE and a control group of 12 participants where a clinically suspected NCSE was not confirmed. The NCSE detection system was built and validated on the training and testing dataset in the NCSE group, respectively. Around 15 features were mainly extracted from the time and frequency domains of EEG signals [2]. We trained a 3-class RUSBoost classifier to score each epoch (2.56 seconds) in three categories: ictal, abnormal activities, and normal activities. The abnormal activities mainly indicate the electrographic activity during the transition between ictal and normal activities. The decision of the ictal or normal-activity event was based on the evolution of three-category scores in 20-second window. As a preliminary result, a 5-fold cross validation method was executed to achieve the classification performance within one subject. About 85% of ictal events could be detected using our system, and its precision achieves 78%. The performance of each participant will be presented in future work. Non-convulsive status epilepticus (NCSE) is an epileptic process, where electrographic seizure activity persists over 10 minutes without noticeable motor symptoms [1]. Long-term NCSE with high degree of unresponsiveness may result in structural brain damage for the ICU patients. During the patients’ hospital stay, it is practically difficult to constantly make precise diagnosis of NCSE by clinicians via a routine procedure. Given the subtle and variable clinical symptoms, clinicians widely use electroencephalography (EEG) to diagnose NCSE. The ictal discharges during NCSE are visually analyzed by the clinicians based on some common morphological EEG patterns. However, the visual inspection by humans is time-consuming and subjective. Moreover, the safety of the chronic patients with NCSE is not guaranteed without proper monitoring. Daily monitoring of these patients unduly burdens their caregivers. Therefore, a 24/7 automatic NCSE detection system via continuous EEG signals is desirable at both hospital and home. We aim to develop a ‘brainwave’ chip, which can constantly monitor the EEG signals from NCSE patients. An automatic NCSE detection algorithm applied on this chip is investigated. This is a retrospective observational study with existing EEG and one-lead ECG recordings from two groups: 16 participants with a clinical diagnosis of NCSE and a control group of 12 participants where a clinically suspected NCSE was not confirmed. The NCSE detection system was built and validated on the training and testing dataset in the NCSE group, respectively. Around 15 features were mainly extracted from the time and frequency domains of EEG signals [2]. We trained a 3-class RUSBoost classifier to score each epoch (2.56 seconds) in three categories: ictal, abnormal activities, and normal activities. The abnormal activities mainly indicate the electrographic activity during the transition between ictal and normal activities. The decision of the ictal or normal-activity event was based on the evolution of three-category scores in 20-second window. As a preliminary result, a 5-fold cross validation method was executed to achieve the classification performance within one subject. About 85% of ictal events could be detected using our system, and its precision achieves 78%. The performance of each participant will be presented in future work

Neurophysiological correlates of increased verbal working memory in high-dissociative participants: a functional MRI study

Background Dissociation, defined as a disruption in usually integrated mental functions, is found not only in DSM-IV dissociative disorders, but also in post-traumatic stress disorder and eating disorders. Dissociative phenomena are also common in the general population, and may reflect a constitutionally determined cognitive style rather than a pathological trait acquired through experiencing adverse life events. In pathological dissociation, evidence has been presented for episodic memory dysfunction. In contrast, in high-dissociative subjects increased performance has been found for episodic memory and dual task performance. These findings have been linked to changes in working memory capacity. Method In the present study, the authors sought to extend these findings by using functional magnetic resonance imaging during performance of two parametric working memory tasks. We tested 21 healthy low- and high-dissociative participants. Results High-dissociative participants performed slightly better during both tasks. Imaging data showed that both groups activated similar networks for both tasks, i.e. (bilateral) dorsolateral (DL) and ventrolateral prefrontal cortex (PFC), parietal cortex, and supplementary motor area. Group x task interactions were found in the high-dissociative group in L DLPFC and L parietal cortex; in the low-dissociative group in R fusiform gyrus. The differences in the high-dissociative group were independent from performance differences, implying that high-dissociative subjects generally recruit this network to a greater extent. Conclusions These results confirm earlier findings using a verbal WM task in high-dissociative participants, and are compatible with the conceptualization of non-pathological dissociation as an information-processing style, characterized by distinct attentional and mnemonic abilities