AUTHOR=Glaba Pawel , Latka Miroslaw , Krause Małgorzata J. , Kroczka Sławomir , Kuryło Marta , Kaczorowska-Frontczak Magdalena , Walas Wojciech , Jernajczyk Wojciech , Sebzda Tadeusz , West Bruce J. 

TITLE=Absence Seizure Detection Algorithm for Portable EEG Devices

JOURNAL=Frontiers in Neurology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2021.685814

DOI=10.3389/fneur.2021.685814

ISSN=1664-2295

ABSTRACT=Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and
termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs)
in EEG are their characteristic manifestations. This type of seizure is severe in two common
pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance
of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of
CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating
diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein,
we present a novel absence detection algorithm based on the properties of the complex Morlet
continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients
(37 hours of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 hours
of recordings) for development and testing. For seizures lasting longer than two seconds, the
detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity
of 97.6% with 0.7/hour detection rate. In the patients, all false detections were associated with
epileptiform discharges, which did not yield clinical manifestations. When the duration threshold
was raised to three seconds, the false detection rate fell to 0.5/hour. The overlap of automatically
detected seizures with the actual seizures was equal to ∼96%. For EEG recordings sampled
at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10
(about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.