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Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [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 care assistance (regular hospital admission or intensive care unit admission), during the course of their illness, using only demographic and clinical data. For this research, a data set of 10,454 patients from 14 hospitals in Galicia (Spain) was used. Each patient is characterized by 833 variables, two of which are age and gender and the other are records of diseases or conditions in their medical history. In addition, for each patient, his/her history of hospital or intensive care unit (ICU) admissions due to CoVid-19 is available. This clinical history will serve to label each patient and thus being able to assess the predictions of the model. Our aim is to identify which model delivers the best accuracies for both hospital and ICU admissions only using demographic variables and some structured clinical data, as well as identifying which of those are more relevant in both cases. The results obtained in the experimental study show that the best models are those based on oversampling as a preprocessing phase to balance the distribution of classes. Using these models and all the available features, we achieved an area under the curve (AUC) of 76.1% and 80.4% for predicting the need of hospital and ICU admissions, respectively. Furthermore, feature selection and oversampling techniques were applied and it has been experimentally verified that the relevant variables for the classification are age and gender, since only using these two features the performance of the models is not degraded for the two mentioned prediction problems. This research has been supported by GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry, Xunta de Galicia grant COV20/00604 through the ERDF Funds. Also, it has been possible thanks to the support of the Xunta de Galicia (Dirección Xeral de Saúde Pública) by providing the anonymous patient data. Also, it has been supported by the Xunta de Galicia (Grant ED431C 2018/34 and IN845D 2020/26 of the Axencia Galega de Innovación) with European Union ERDF funds. CITIC, as Research Center accredited by Galician University System, is funded by Consellería de Cultura, Educación e Universidades from Xunta de Galicia, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by Secretaría Xeral de Universidades (Grant ED431G 2019/01). Finally, we would also like to thank Prof. Ricardo Cao, as Chairman of the Committee of Experts for Mathematical Action against Coronavirus, for his kind request to collaborate in this project Xunta de Galicia; COV20/00604 Xunta de Galicia; ED431C 2018/34 Xunta de Galicia; IN845D 2020/26 Xunta de Galicia; ED431G 2019/01

Trátase dun resumo estendido da ponencia [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 night sleep study can take several hours. Most of the automatic methods trying to solve this problem use human engineered features biased for a specific dataset. In this work we use deep learning to avoid human bias. We propose an ensemble of 5 convolutional networks achieving a kappa index of 0.83 when classifying 500 sleep studies. Xunta de Galicia; ED431G/01

The success of any Intrusion Detection System (IDS) lies in the selection of a set of significant features, that can be quantitative or qualitative, taken out from a network traffic data stream. The machine learning methods provide potential solutions for the IDS problem. However, most of these methods used for classification are not able to handle symbolic attributes directly. In this paper, three methods for symbolic features conversion - indicator variables, conditional probabilities and the Separability Split Value method - are contrasted with the arbitrary conversion method, all of them applied to an intrusion detection problem, the KDD Cup 99 data set. In particular, three classification methods were subsequently applied to the dataset: a one-layer feedforward neural network, a support vector machine and a multilayer feedforward neural network. The results obtained demonstrate that the three conversion methods improve the prediction ability of the classifiers utilized, with respect to the arbitrary and commonly used assignment of numerical values.

The final publication is available at ScienceDirect via http://dx.doi.org/10.1016/j.ins.2015.08.022 [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 Electroencephalogram. With this in mind, the capabilities of several machine learning techniques were tried. The inputs for the models were a set of features based on amplitude and duration measurements obtained from waveforms to be classified. Among all the classifiers tested, the Support Vector Machine obtained the best results with an accuracy of 88.69%. Finally, to enhance the generalization capabilities of the classifiers, while at the same time discarding the existing irrelevant features, feature selection methods were employed. After this process, the classification performance was significantly improved. The best result was obtained applying a correlation-based filter, achieving a 91.40% of accuracy using only 36% of the total input features. Xunta de Galicia; 09SIN003CT Ministerio de Economía y Competitividad; TIN2013-40686P Ministerio de Economía y Competitividad; TIN2012-37954 Xunta de Galicia; GRC2014/35

An effective diagnosis of the sleep apnea syndrome (SAS) is based on a contextual analysis of the patient's polysomnograph, consisting of simultaneously recording electrophysiological and pneumological signals during a night's sleep. Currently, the prevalence of this disorder has caused an increase in the demand for specialist clinical assistance and sleep units. As in other areas of medicine, the volume of clinical data that has to be processed is enormous, which justifies the construction of computerized decisionmaking tools that partially automate these routine tasks. Our system, SAMOA, belongs to this category of help tools, being an automatic SAS diagnostic system that incorporates both conventional programming and artificial intelligence techniques. This article describes the most important aspects of the temporal data management in the different analysis processes and the final correlation of all the symbolic information generated by the different cooperative modules.

The sleep apnea syndrome (SAS) is a respiratory disorder, which is characterised by the occurrence of five or more apneic events (apnea or hypopnea) per hour of sleep. Diagnosis of the SAS is a process that is markedly heuristic by nature, in that doctors handle information that is both numerical and symbolic, and employ qualitative descriptive terminology. An expert draws up a contextualised clinical interpretation that relates a patient's sleep process and respiratory physiology, involving a detailed analysis of the polysomnograph corresponding to a night's sleep. This task, implying a great deal of work on the part of clinical staff and a high economic cost, can in fact be partially automated. Our paper describes a modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph. The main tasks of our system are the identification and classification of respiratory events, the construction of the patient's hypnogram and the correlation of all the information obtained so as to arrive at a final diagnosis with respect to the existence of the syndrome. Finally our article presents and discusses the results obtained following a preliminary validation of the developed system.

Automation of the medical diagnosis of the Sleep Apnea Syndrome(SAS) requires an intelligent analysis of the pneumological and neurophysiological signals of the patient that combines both conventional and Artificial Intelligence techniques in order to detect respiratory abnormalities and construct a hypnogram for the patient, and a process of temporal fusion and correlation between the signals for both a correct classification of the apneic events within a sleep stage framework, and to explain the occurrence of abnormal sleep patterns as a consequence of these events. In this article, the most important aspects of the analysis and information integration processes are described and the preliminary validation results obtained are discussed.