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Convolutional Autoencoder and Deep Infomax for epileptic EEG signals clustering.

Alessia Toscano

Convolutional Autoencoder and Deep Infomax for epileptic EEG signals clustering.

Rel. Gabriella Olmo, Monica Visintin. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2019

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Abstract:

Convolutional Autoencoder and Deep Infomax for epileptic EEG signals clustering. This work of thesis wants to show how Convolutional Autoencoder and Deep Infomax networks can help in the diagnosis of epilepsy, because nowadays the duty belongs to five doctors. Different tests have been effectuated using two different networks and applying different initial conditions such as: epoch length, number of channel observed and different composition of traning set. Unluckily, hoped results have not been achieved, but important starting points for furhter developments are presented.

Relators: Gabriella Olmo, Monica Visintin
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 62
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Biomedica
Classe di laurea: New organization > Master science > LM-21 - BIOMEDICAL ENGINEERING
Ente in cotutela: Mondragon Unibertsitatea-Faculty of Engineering (SPAGNA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/12303
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