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

[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 para la interpretación contextual de las anormalidades respiratorias en el marco de la fase de sueño del paciente, En este trabajo se plantea un sistema organizado en 3 modulos. En primer lugar, un modulo encargado de la evaluación previa del paciente a través de un sencillo cuestionario que permita al clinico obtener información sufiente que justifique la conveniencia de realizar una polisomnografia. En segundo lugar, un módulo de construccion de hipnograma, o mapa de sueño del paciente, que permita identificar las fases por las que ha discurrido, su duración y las transiciones que hayan tenido lugar. De esta forma, la integración final de la información de estos dos últimos modulos facilita la interpretación de los eventos observados. Para la evaluación previa del paciente y de entre los criterios publicados en la literatura, se ha elegido el protocolo de autoevaluación propuesto por Johnson y Halberstadt. Para la evaluación global del protocolo se utiliza el modelo de Shortliffe y Buchanan de factores de certeza en el que no se consideran evidencias negativas. Los umbrales de deteccion utilizados son de 0,8 para aquellas preguntas que representan una mayor contribución para la recomendación de realizar el analisis; y de 0,2 para el resto de preguntas del test. Una vez que se ha completado el cuestionario y se conoce que evidencias apoyan a la hipotesis de Prescripcion polisomnografica, se calcula el valor del factor de certeza resultante clasificándolo lingüisticamente

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

The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-474-9-213[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 find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best performance regardless the imputation method used

Automatic classification of respiratory patterns involving missing data imputation techniques

[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 find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that in general, the Self-Organising Map imputation method allows non-tree based classifiers to achieve improvements over the rest of the imputation methods in terms of the classification accuracy, and that the Feedforward neural network and the Random Forest classifiers offer the best performance regardless of the imputation method used. The improvements in terms of accuracy over the previous work of the authors are limited but the Feed Forward neural network model achieves promising results.Ministerio de Economía y Competitividad; TIN 2013-40686-PXunta de Galicia; GRC2014/35

A convolutional network for the classification of sleep stages

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/0

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

[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 classification of the tools in which the categories are organized following the development lifecycle of an ML system and we make a review of the existing tools within each section of the classification. We believe this will help to better understand the ecosystem of tools currently available and will also allow us to identify niches in which to develop new tools to aid in the development of AI and ML systems. After reviewing the state-of-the-art of the tools, we have identified three trends in them: the incorporation of humans into the loop of the machine learning process, the movement from ad-hoc and experimental approaches to a more engineering perspective and the ability to make it easier to develop intelligent systems for people without an educational background in the area, in order to move the focus from the technical environment to the domain-specific problem.This work has been supported by the State Research Agency of the Spanish Government, grant (PID2019-107194GB-I00 / AEI / 10.13039/501100011033) and by the Xunta de Galicia, grant (ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund-Galicia 2014-2020 Program), by grant ED431G 2019/01Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G 2019/0

Automatic detection of EEG arousals

[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 method using signal processing and machine learning models, for arousal detection, is presented. Relevant events are identified in the EEG signals and in the electromyography, during the signal processing phase. After discarding those events that do not meet the required characteristics, the resulting set is used to extract multiple parameters. Several machine learning models — Fisher’s Linear Discriminant, Artificial Neural Networks and Support Vector Machines — are fed with these parameters. The final proposed model, a combination of the different individual models, was used to conduct experiments on 26 patients, reporting a sensitivity of 0.72 and a specificity of 0.89, while achieving an error of 0.13, in the arousal events detection.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/035Ministerio de Economía y Competitividad; TIN2013-40686

A Convolutional Network for Sleep Stages Classification

[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 trained expert can spend several hours scoring a single night recording. Multiple automatic methods have tried to solve these problems in the past, most of them by classifying a feature vector that is engineered for a specific dataset. In this work, we avoid this bias using a deep learning model that learns relevant features without human intervention. Particularly, we propose an ensemble of 5 convolutional networks that achieves a kappa index of 0.83 when classifying a dataset of 500 sleep recordings

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

doi: 10.2174/1874431101408010001This 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 arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints

Experiencias en la implantación de la asignatura de Tecnología de Programación al EEES

En este artículo se describen las experiencias realizadas para la adaptación al Espacio Europeo de Educación Superior de la asignatura Tecnología de la Programación, de la Ingeniería Técnica en Informática de Sistemas de la Universidad de A Coruña. Además, se presenta la guía docente de la asignatura, los resultados de las encuestas realizadas a los alumnos sobre el nuevo sistema empleado y un análisis crítico de los problemas encontrados

Adaptación de la asignatura Fundamentos de Informática de la Ingeniería Técnica Industrial al Espacio Europeo de Educación Superior

En este artículo se presentan las experiencias realizadas para la adaptación al Espacio Europeo de Educación Superior de la asignatura Fundamentos de Informática, de la Ingeniería Técnica Industrial en la Universidad de A Coruña. Además, se presenta el proyecto de guía docente de la asignatura, los problemas encontrados y algunos resultados obtenidos en el proceso de adaptación. Finalmente, se expone la propuesta para el curso 2005/06 a partir de estas experiencias