Generalized tonic-clonic seizures are accompanied by changes of interrelations within the autonomic nervous system

Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy

BACKGROUND

Predicting seizure likelihood for the following day would enable clinicians to extend or potentially schedule video-electroencephalography (EEG) monitoring when seizure risk is high. Combining standardized clinical data with short-term recordings of wearables to predict seizure likelihood could have high practical relevance as wearable data is easy and fast to collect. As a first step toward seizure forecasting, we classified patients based on whether they had seizures or not during the following recording.

METHODS

Pediatric patients admitted to the epilepsy monitoring unit wore a wearable that recorded the heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and peripheral body temperature. We utilized short recordings from 9:00 to 9:15 pm and compared mean values between patients with and without impending seizures. In addition, we collected clinical data: age, sex, age at first seizure, generalized slowing, focal slowing, and spikes on EEG, magnetic resonance imaging findings, and antiseizure medication reduction. We used conventional machine learning techniques with cross-validation to classify patients with and without impending seizures.

RESULTS

We included 139 patients: 78 had no seizures and 61 had at least one seizure after 9 pm during the concurrent video-EEG and E4 recordings. HR (P < 0.01) and EDA (P < 0.01) were lower and HRV (P = 0.02) was higher for patients with than for patients without impending seizures. The average accuracy of group classification was 66%, and the mean area under the receiver operating characteristics was 0.72.

CONCLUSIONS

Short-term wearable recordings in combination with clinical data have great potential as an easy-to-use seizure likelihood assessment tool.

Dysautonomia in people with epilepsy: A scoping review

BACKGROUND

Epilepsy is one of the most common neurological diseases and has high morbidity and mortality. Multiple methods for assessing dysautonomia have been reported; however, the patient characteristics and epilepsy features that drive any method selection are unclear. People with epilepsy (PWE) can experience sudden unexpected death in epilepsy (SUDEP) and one reason can be dysautonomia. If dysautonomia can be detected in PWE before a severe event, then it could complement and redirect patient treatment and monitoring.

OBJECTIVE

To map the available literature on dysautonomia in PWE and describe patients' characteristics and methods used to evaluate dysautonomia.

METHODS

We performed a scoping literature review. We searched PubMed, Scopus, Embase, and hand searched starting from the first registry in the literature until August 2019. Studies were independently assessed by three authors and two epileptologists. We present data in tables and summarize information according to the following structure: population, concepts, and context.

RESULTS

Thirty-five studies were included in the analysis with epidemiological designs including case reports (23), cross-sectional studies (4), case‒controls (7), and cohort studies (1). A total of 618 patients were enrolled. Heart rate variability, arrhythmia, blood pressure, the tilt-table test, polysomnography, respiratory function, and magnetic resonance imaging were the methods most commonly used to assess dysautonomia in PWE. A detailed description of the heart rate variability assessment is presented.

CONCLUSIONS

This review provides a broad description of the available literature identifying clinical findings, the most frequently reported assessment measurements of dysautonomia, in temporal lobe epilepsy and extratemporal epilepsies.

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.

Electrodermal activity response during seizures: A systematic review and meta-analysis

INTRODUCTION

Wearable devices for continuous seizure monitoring have drawn increasing attention in the field of epilepsy research. One of the parameters recorded by these devices is electrodermal activity (EDA). The aim of this study was to systematically review the literature to estimate the incidence of electrodermal response during seizures.

METHODS

We searched all articles recording concurrent EDA and EEG activity during the pre-ictal, ictal, and postictal periods in children and adults with epilepsy. Studies reporting the total number of seizures and number of seizures with an EDA response were included for a random-effects meta-analysis.

RESULTS

Nineteen studies, including 550 participants and 1115 seizures were reviewed. All studies demonstrated an EDA increase during the ictal and postictal periods, while only three reported pre-ictal EDA responses. The meta-analysis showed a pooled EDA response incidence of 82/100 seizures (95% CI 70-91). Tonic-clonic seizures (both generalized tonic-clonic seizures (GTCS) and focal to bilateral tonic-clonic seizures (FBTCS)) elicited a more pronounced (higher and longer-lasting) EDA response when compared with focal seizures (excluding FBTCS).

DISCUSSION

Epileptic seizures produce an electrodermal response detectable by wearable devices during the pre-ictal, ictal, and postictal periods. Further research is needed to better understand EDA changes and to analyze factors which may influence the EDA response.