Absence Seizure Detection Algorithm for Portable EEG Devices

Frontiers in Neurology

Wojciech Jernajczyk¹  Miroslaw Latka²  Pawel Glaba²  Sławomir Kroczka³  Tadeusz Sebzda⁴  Małgorzata J. Krause⁵  Marta Kuryło⁵  Bruce J. West⁶  Wojciech Walas⁷  Magdalena Kaczorowska-Frontczak⁸ 

[1] Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warszawa, Poland;Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland;Department of Child Neurology, Jagiellonian University Medical College, Krakow, Poland;Department of Pathophysiology, Wroclaw Medical University, Wroclaw, Poland;Department of Pediatric Neurology, T. Marciniak Hospital, Wrocław, Poland;Office of the Director, Army Research Office, Research Triangle Park, Durham, NC, United States;Paediatric and Neonatal Intensive Care Unit, Institute of Medical Sciences, University of Opole, Opole, Poland;The Children's Memorial Health Institute, Warszawa, Poland;

关键词: childhood absence epilepsy;    EEG;    wavelets;    detector;    portable device;   

DOI  :  10.3389/fneur.2021.685814

来源: DOAJ

【 摘 要 】

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 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h 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 3 s, the false detection rate fell to 0.5/h. 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.

【 授权许可】

Unknown