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

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.
9781509007479
Automatic detection; Autosomal dominants; Complex inputs; Electroencephalographic signals; Frontal lobes; Manifold learning; Polysomnography
File in questo prodotto:
File Dimensione Formato  
pisano barbara IEEE.pdf

Solo gestori archivio

Tipologia: versione editoriale
Dimensione 409.04 kB
Formato Adobe PDF
409.04 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/195237
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 0
social impact