Summary Objective Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24‐h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time‐consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two‐channel behind‐the‐ear EEG‐based wearable, the Sensor Dot (SD), to detect typical absences in adults and children; and (2) develop a sensitive patient‐specific absence seizure detection algorithm to reduce the review time of the recordings. Methods We recruited 12 patients (median age = 21 years, range = 8–50; seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24‐h 25‐channel video‐EEG recording to assess their refractory typical absences. Four additional behind‐the‐ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3‐Hz spike‐and‐wave discharges on EEG, lasting 3 s or longer. Seizures on SD were blindly annotated on the full recording and on the algorithm‐labeled file and consequently compared to 25‐channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score. Results We concomitantly recorded 284 absences on video‐EEG and SD. Our absence detection algorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm‐labeled files resulted in scores of .83, .89, and .87, respectively. Patient self‐reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15. Significance Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detection algorithm reduced the review time of a 24‐h recording from 1‐2 h to around 5–10 min.

Patients with absence epilepsy fail to report almost 90% of their seizures. The clinical gold standard to assess absence seizures is video-electroencephalography (vEEG) recorded in the hospital, an expensive and obtrusive procedure which requires also extended reviewing time. Wearable sensors, which allow the recording of electroencephalography (EEG), accelerometer and gyroscope have been used to monitor epileptic patients in their home environment for the first time. We developed a pipeline for accurate and robust absence seizure detection while reducing the review time of the long recordings. Our results show that multimodal analysis of absence seizures can improve the robustness to false alarms, while retaining a high sensitivity in seizure detection.

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Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.