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Improving detection of apneic events by learning from examples and treatment of missing data
(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 ...
A comparison of performance of K-complex classification methods using feature selection
(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 ...
Automatic classification of respiratory patterns involving missing data imputation techniques
(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 ...
Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome
(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 ...
Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach
(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 ...
FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks
(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, ...
A Convolutional Network for Sleep Stages Classification
(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 ...
Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19
(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 ...
Human-in-the-loop machine learning: a state of the art
(Springer Nature, 2023-04)
[Abstract]: Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, ...