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. 2021 Nov;62(11):2741-2752.
doi: 10.1111/epi.17061. Epub 2021 Sep 7.

Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears

Lauren Swinnen  1 , Christos Chatzichristos  2 , Katrien Jansen  3 , Lieven Lagae  3 , Chantal Depondt  4 , Laura Seynaeve  5   6 , Evelien Vancaester  7 , Annelies Van Dycke  8 , Jaiver Macea  1 , Kaat Vandecasteele  2 , Victoria Broux  1 , Maarten De Vos  2   3 , Wim Van Paesschen  1
Affiliations

Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears

Lauren Swinnen et al. Epilepsia. 2021 Nov.

Abstract

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.

Keywords: epilepsy; seizure detection algorithm; seizure underreporting; typical absence seizures; wearable seizure detection.

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Conflict of interest statement

L.L. has received speaker honoraria from and is participating on advisory boards for Zogenix, Livanova, UCB, Eisai, Novartis, NEL, and Epihunter. None of the other authors has any conflict of interest to disclose. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Figures

FIGURE 1
FIGURE 1
Concept of the Sensor Dot (SD) used as a wearable to detect absence seizures. (1) Four electrodes (in orange) are placed behind the ears of the patient and connected to the mobile electroencephalographic (EEG) device, the SD, which is attached to the upper back via an adhesive (in blue). An enlarged image of the SD is given in the circle. (2) After 24 h of recording, the SD is placed in the docking station, which allows recharging of the battery. In addition, when the SD is in the docking station, the SD EEG data are automatically uploaded to the cloud via a Wi‐Fi connection. (3) Afterward, the absence detection algorithm analyzes the data and flags segments of interest (in red). (4) Finally, the flagged data are sent back to the treating neurologist, who can then review the flagged SD EEG data in a short time.
FIGURE 2
FIGURE 2
Examples of 3‐Hz spike‐and‐wave discharges visible on the two‐channel Sensor Dot during an absence seizure in (A) a pediatric patient and (B) an adult patient. A high‐pass filter of .53 Hz, a low‐pass filter of 35 Hz, and a notch filter were applied. Sensitivity: 100 µV/cm. Time base: 10 s. Absences lasting 8 s (A) and 5 s (B) were marked. Ch1#1, left; Ch2#1, right
FIGURE 3
FIGURE 3
Common reasons for a false positive (FP) or false negative (FN) annotation on Sensor Dot. (A) Chewing artifact, characterized by 2‐Hz slow waves with superimposed muscle artifacts, which were often mistaken for seizures (FPs). (B, C) Commonly missed absences due to the presence of chewing artifacts (B) and muscle artifacts (C). A high‐pass filter of .53 Hz, a low‐pass filter of 35 Hz, and a notch filter were applied. Sensitivity: 100 µV/cm. Time base: 10 s. Ch1#1, left; Ch2#1, right
FIGURE 4
FIGURE 4
Percentage of seizures (defined in this study as a discharge lasting 3 s or longer) of different duration reported by the patients themselves or by caregivers for children. (A) Each duration separately. (B) Grouped into shorter and longer duration in relation to the findings by Guo et al. EEG, electroencephalographic
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