Automatic Absence Seizures Detection in EEG signals: An Unsupervised Module.

Absence seizures are expressed with distinctive spike-and-wave complexes in the electroencephalogram (EEG), which can be used to automatically distinguish them from other types of seizures and interictal activity. Considering the chaotic nature of the EEG signal, it is very unlikely that such continuous, repetitive patterns with strict periodic behavior would occur naturally under normal conditions. Searching for spectral activity in the range of 2.5-4.5 Hz and assessing the presence of synchronous, repeated patterns across multiple EEG channels in an unsupervised manner, the proposed methodology provides high absence seizure detection sensitivity of 93.94% with a low false detection rate of 0.168 FD/h using the open TUSZ dataset.