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A convolutional network for the classification of sleep stages
(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 ...
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 ...
Automatic detection of EEG arousals
(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. ...
A classification and review of tools for developing and interacting with machine learning systems
(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 ...
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 ...
