Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing

Abstract

Objective: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device.As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. Methods:We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one-channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded fullarray video-EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video-EEGs. Results:We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%-88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%-100%). The average false detection rate was .53/h (95% CI = .32-.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was .823 (IQR = .57-1). For the total recording duration, F1 score was .74.We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures.

Design of hydrogel-based wearable EEG electrodes for medical applications

“…The device deficiency was 4.67% and the average sensitivity per patient was 78.83%. 193 The device even correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures, which is 100% in the automated behavioral testing in 36 seizures. Alongside the wearable system, there have been tremendous efforts dedicated to developing generalizable machine learning (ML) algorithms to extract information of epileptic activity in EEG data as well.…”

“…The device deficiency was 4.67% and the average sensitivity per patient was 78.83%. 193 The device even correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures, which is 100% in the automated behavioral testing in 36 seizures.…”

“…192 (c) The BrainLink EEG epileptic seizure monitoring system uses a headband to acquire bipolar signals from the dry electrodes on FP1 and F7. 193 The ear-EEG they used was an earbud-based sensor, 202 which has a similar design as the earplug EEG device that mentioned in Fig. 8(a).…”

“…An enlarged image of the SD is given in the circle 192. (c) The BrainLink EEG epileptic seizure monitoring system uses a headband to acquire bipolar signals from the dry electrodes on FP1 and F7 193.…”

Multimodal detection of typical absence seizures in home environment with wearable electrodes

“…Furthermore, there are challenges regarding the signal quality and the time needed by the neurologists to annotate the data. Hence, research for automated seizure detection algorithms, tailored for wearable EEG, is still in its infancy, especially in wearable detection of absence seizures (Kjaer et al, 2017;Dan et al, 2020;Swinnen et al, 2021;Japaridze et al, 2022), (semi) automated seizure detection would facilitate the adoption of EEG wearables in clinical practice. Furthermore, even the annotation of wearable EEG data, compared to vEEG, is not trivial (Zhang et al, 2022).…”