Dataset Information

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

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.

SUBMITTER: Swinnen L

PROVIDER: S-EPMC9292701 | biostudies-literature |

REPOSITORIES: biostudies-literature

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