Bruno, Elisa

Abstract Objective Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri‐ictal periods in performing a feature‐based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. Methods This study assesses the signal quality of wearable data during peri‐ictal phases of generalized tonic–clonic and focal to bilateral tonic–clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri‐ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. Results We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre‐ and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Significance Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist‐worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

AbstractObjectiveEpilepsy 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.MethodsUsing 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.ResultsThe 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.SignificanceThe 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.

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.