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

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 full-array 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. SIGNIFICANCE: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices.

Lessons learned from the RE(ACT) conference on medical devices for rare diseases.

The field of rare disease therapeutics has witnessed significant growth in recent years, highlighting the need for diverse therapeutic approaches to cater to the unique needs of individuals with rare diseases. Rare disease therapies encompass a broad spectrum of interventions, including orphan medicinal products, orphan medical devices, rehabilitative therapies, and digital therapeutics, with the lines between these categories blurring. This paper covers the session of the RE (ACT)-IRDiRC Conference 2023 and delves into the landscape of orphan medical device research and development, shedding light on the challenges and opportunities in this burgeoning field. It provides a short overview of the different international legislations in the field. In addition, it highlights several exemplary orphan medical devices. The first example is an exoskeleton for boys with Duchenne Muscular Dystrophy, enabling them to maintain arm functionality and independence. Another example presented was an EEG device linked to an app detecting seizures in rare epilepsy conditions, which alerts caregivers to seizures in real-time but also facilitates objective seizure reporting for clinicians, aiding in diagnosis and treatment optimization. It also showcases the role of gamification and enabling technologies in addressing rare diseases, by showing a game designed for children with cystic fibrosis, and a telemedicine system for rehabilitation therapy. Both solutions aim to improve patients' understanding of their conditions and enhance their self-management. In conclusion, this paper underscores the critical need for patient-centric orphan and pediatric medical devices to provide therapeutic options for individuals with rare diseases. It highlights the impact of existing devices on enhancing the quality of life for rare disease patients and emphasizes the necessity for greater incentives and support for research and development in this field.