• Feature selection with limited bit depth mutual information for embedded systems 

  Morán-Fernández, Laura; Bolón Canedo, Verónica; Alonso-Betanzos, Amparo (M D P I AG, 2018-09-17)

  [Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ...

• A convolutional network for the classification of sleep stages 

  Fernández-Varela, Isaac; Hernández Pereira, Elena; Moret Bonillo, Vicente (M D P I AG, 2018-09-14)

  [Abstract] The classification of sleep stages is a crucial task in the context of sleep medicine. It involves the analysis of multiple signals thus being tedious and complex. Even for a trained physician scoring a whole ...

• Interpretable market segmentation on high dimension data 

  Eiras-Franco, Carlos; Guijarro-Berdiñas, Berta; Alonso-Betanzos, Amparo; Bahamonde, Antonio (M D P I AG, 2018-09-17)

  [Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...

• Improving detection of apneic events by learning from examples and treatment of missing data 

  Hernández Pereira, Elena; Álvarez Estévez, Diego; Moret Bonillo, Vicente (I O S Press, 2014)

  [Abstract] This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to ...

• Automatic detection of EEG arousals 

  Fernández-Varela, Isaac; Hernández Pereira, Elena; Álvarez Estévez, Diego; Moret Bonillo, Vicente (ESANN, 2016-04-27)

  [Abstract] Fragmented sleep is commonly caused by arousals that can be detected with the observation of electroencephalographic (EEG) signals. As this is a time consuming task, automatization processes are required. ...

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