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.

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

PISANO, BARBARA;CANNAS, BARBARA;MONTISCI, AUGUSTO;PISANO, FABIO;PULIGHEDDU, MONICA MARIA FRANCESCA;SIAS, GIULIANA;FANNI, ALESSANDRA
2016-01-01

Abstract

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.
2016
9781509007479
Automatic detection; Autosomal dominants; Complex inputs; Electroencephalographic signals; Frontal lobes; Manifold learning; Polysomnography
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/195237
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