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
Inglese
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)
9781509007479
Institute of Electrical and Electronics Engineers (IEEE)
New York
STATI UNITI D'AMERICA
1
6
6
26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Contributo
Esperti anonimi
13-16 September 2016
Vietri sul Mare (Salerno), Italy
internazionale
scientifica
Automatic detection; Autosomal dominants; Complex inputs; Electroencephalographic signals; Frontal lobes; Manifold learning; Polysomnography
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Pisano, Barbara; Cannas, Barbara; Milioli, G.; Montisci, Augusto; Pisano, Fabio; Puligheddu, MONICA MARIA FRANCESCA; Sias, Giuliana; Fanni, Alessandra ...espandi
273
8
4.1 Contributo in Atti di convegno
reserved
info:eu-repo/semantics/conferencePaper
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