In this paper, a Manifold Learning approach for the automatic detection of Autosomal Dominant Nocturnal Frontal Lobe Epilepsy seizures is presented, with the aim to support neurologists in the labelling efforts. Features extracted from polysomnography signals are used in order to detect and discriminate seizure epochs. This task has been addressed by mapping the electroencephalographic signal epochs in different regions of the features space. The result is a Self Organizing Map, which allows to investigate over not straightforward relations in the complex input space for the characterization of seizures.
['B. Pisano', 'B. Cannas', 'Giulia Milioli', 'Augusto Montisci', 'Fabio Pisano', 'Monica Puligheddu', 'G Sias', 'A. Fanni']
Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of eeg records and self organizing maps