Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of eeg records and self organizing maps

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.

1. Introduction

Autosomal Dominant Nocturnal Frontal Lobe Epilepsy (ADNFLE) is a genetically heterogeneous disorder that usually occurs at night when an affected person is sleeping. Its symptoms may include sudden nocturnal awakenings or eyes opening, bizarre placement of arms and/or legs, frightened or surprised facial expressions, screams, dystonic and tonic postures, and clonic limb convulsions [1].