Schulze-Bonhage, Andreas

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.

SummaryMany patients with epilepsy caused by hypothalamic hamartomas (HHs) have cognitive impairments during the course of the disease or following neurosurgical treatment. The purpose of this study was to assess cognitive function in these patients, as well as factors influencing preoperative cognitive performance and cognitive outcome after neurosurgical treatment. Using the two largest and most detailed neuropsychology datasets on HH and epilepsy from two centers, we retrospectively report on cognitive functions in 48 patients with structural epilepsy due to HH (mean age ± standard deviation [SD] 20 ± 12 years, range 5–53 years, median 16 years; disease duration mean 17 ± 11 years). Intelligence, verbal learning and recall, and speed and executive functions (processing speed and cognitive flexibility) were assessed before and on average 19 (±11) months after surgery (interstitial radiosurgery: N = 22; neurosurgical resection/disconnection: N = 26). Prior to neurosurgical treatment, 52% of patients showed impaired executive and 62% showed reduced verbal memory functions. A trend for a detrimental effect of higher drug load on cognitive functioning was found. After neurosurgical treatment, intellectual functions for the entire cohort tended to increase. This correlated with improved seizure frequency and decreased number of antiepileptic drugs (AEDs). However, postoperative outcomes for individual patients were highly variable, with significant deteriorations in 17% (processing speed) to 34% (cognitive flexibility and verbal learning), and performance increases in 17% (intellectual functioning) up to 39% (processing speed) of the patients. Higher levels of presurgical performance were significant predictors of cognitive decline after surgery. These results are highly relevant for patient consultation and may help with therapeutic decisions.