Long-term video-EEG monitoring has improved diagnosis and treatment of epilepsy, especially in children. However, the amount of data neurophysiologists must analyze has grown remarkably. The main purpose of this paper is to provide a diagnostic support to speed up and ease EEG interpretation for a specific application concerning absence seizures, a type of non-motor generalized epileptic seizures. The proposed method consists of a pre-processing step where signals are filtered through the Stationary Wavelet Transform for the reduction of possible artefacts. Subsequently, a supervised automatic classification method is implemented for seizure detection, based on the Support Vector Machine Fine Gaussian method. Finally, a post-processing step is implemented in which spatial and temporal thresholds are defined for both online and offline application. In addition, a method that applies sonification techniques is developed. Sonification techniques could speed up the process of interpreting information, allowing rapid clinical intervention and a continuous monitoring of the event. The dataset consists of 30 EEG recordings performed in 24 children with absence seizures, clinically evaluated at the Meyer Children's Hospital in Firenze, Italy. The method shows encouraging results both in terms of balanced accuracy (about 96%) and latency times (1.25 s on average), which might make it suitable for online clinical trials. In fact, it was implemented in the perspective of a possible real-time application in clinical practice.
Keywords: Absence seizure; Denoising; EEG; Early seizure detection; SWT; Sonification.
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