Listar por autor "Hernández-Pereira, Elena"

Mostrando ítems 1-13 de 13

    • A classification and review of tools for developing and interacting with machine learning systems 

      Mosqueira-Rey, E.; Hernández-Pereira, Elena; Alonso Ríos, David; Bobes-Bascarán, José (Association for Computing Machinery, 2022)
      [Abstract] In this paper we aim to bring some order to the myriad of tools that have emerged in the field of Artificial Intelligence (AI), focusing on the field of Machine Learning (ML). For this purpose, we suggest a ...

    • A comparison of performance of K-complex classification methods using feature selection 

      Hernández-Pereira, Elena; Bolón-Canedo, Verónica; Sánchez-Maroño, Noelia; Álvarez-Estévez, Diego; Moret-Bonillo, Vicente; Alonso-Betanzos, Amparo (2016-01-20)
      [Abstract] The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the ...

    • A Convolutional Network for Sleep Stages Classification 

      Fernández-Varela, Isaac; Hernández-Pereira, Elena; Alvarez-Estevez, Diego; Moret-Bonillo, Vicente (2019-02)
      [Abstract]: Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the ...

    • 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 ...

    • Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach 

      Mosqueira-Rey, E.; Hernández-Pereira, Elena; Bobes-Bascarán, José; Alonso Ríos, David; Pérez-Sánchez, Alberto; Fernández-Leal, Ángel; Moret-Bonillo, Vicente; Vidal-Ínsua, Yolanda; Vázquez-Rivera, Francisca (Springer Nature, 2023-11)
      [Abstract]: Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty ...

    • Automatic classification of respiratory patterns involving missing data imputation techniques 

      Hernández-Pereira, Elena; Álvarez-Estévez, Diego; Moret-Bonillo, Vicente (Academic Press, 2015-10)
      [Abstract] A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. 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. ...

    • FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks 

      Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha; Hernández-Pereira, Elena; Pérez-Sánchez, Beatriz (Elsevier B.V., 2023)
      [Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, ...

    • 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 ...

    • Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome 

      Moret-Bonillo, Vicente; Álvarez-Estévez, Diego; Fernández-Leal, Ángel; Hernández-Pereira, Elena (Bentham Open, 2014-06-13)
      This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in ...

    • Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19 

      Hernández-Pereira, Elena; Fontenla-Romero, Óscar; Bolón-Canedo, Verónica; Cancela, Brais; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo (Springer, 2022)
      [Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital ...

    • Técnicas de inteligencia artifical e ingeniería del software para un sistema inteligente de monitorización de apneas en sueño 

      Hernández-Pereira, Elena (2000)
      [Resumen] El objetivo de esta tesis consiste en el desarrollo de un sistema automatico off-line para la monitorización y analisis de apneas durante el sueño en el cual se utilizan tecnicas de Inteligencia Artificial ...

    • Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment 

      Bobes-Bascarán, José; Fernández-Leal, Ángel; Mosqueira-Rey, E.; Alonso Ríos, David; Hernández-Pereira, Elena; Moret-Bonillo, Vicente (Universidade da Coruña, Servizo de Publicacións, 2023)
      [Abstract] The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models ...