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

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; 09SIN003CTMinisterio de Economía y Competitividad; TIN2013-40686PMinisterio de Economía y Competitividad; TIN2012-37954Xunta de Galicia; GRC2014/3

Ensembles for feature selection: A review and future trends

© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Bolón-Canedo, V. and Alonso-Betanzos, A. (2019) ‘Ensembles for Feature Selection: A Review and Future Trends’ has been accepted for publication in: Information Fusion, 52, pp. 1–12. The Version of Record is available online at https://doi.org/10.1016/j.inffus.2018.11.008.[Abstract]: Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good results. Normally, it has been commonly employed for classification, but it can be used to improve other disciplines such as feature selection. Feature selection consists of selecting the relevant features for a problem and discard those irrelevant or redundant, with the main goal of improving classification accuracy. In this work, we provide the reader with the basic concepts necessary to build an ensemble for feature selection, as well as reviewing the up-to-date advances and commenting on the future trends that are still to be faced.This research has been financially supported in part by the Spanish Ministerio de Economa y Competitividad (research project TIN 2015-65069-C2-1-R), by the Xunta de Galicia (research projects GRC2014/035 and the Centro Singular de Investigación de Galicia, accreditation 2016–2019, Ref. ED431G/01) and by the European Union (FEDER/ERDF).Xunta de Galicia; GRC2014/035Xunta de Galicia; ED431G/0

Diagnosis of the sleep apnea-hypopnea syndrome : a comprehensive approach through an intelligent system to support medical decision

[Abstract] This doctoral thesis carries out the development of an intelligent system to support medical decision in the diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS). SAHS is the most common disorder within those affecting sleep. The estimates of the disease prevalence range from 3% to 7%. Diagnosis of SAHS requires of a polysomnographic test (PSG) to be done in the Sleep Unit of a medical center. Manual scoring of the resulting recording entails too much effort and time to the medical specialists and as a consequence it implies a high economic cost. In the developed system, automatic analysis of the PSG is accomplished which follows a comprehensive perspective. Firstly an analysis of the neurophysiological signals related to the sleep function is carried out in order to obtain the hypnogram. Then, an analysis is performed over the respiratory signals which have to be subsequently interpreted in the context of the remaining signals included in the PSG. In order to carry out such a task, the developed system is supported by the use of artificial intelligence techniques, specially focusing on the use of reasoning mechanisms capable of handling data imprecision. Ultimately, it is the aim of the proposed system to improve the diagnostic procedure and help physicians in the diagnosis of SAHS.[Resumen] Esta tesis aborda el desarrollo de un sistema inteligente de apoyo a la decisión clínica para el diagnóstico del Síndrome de Apneas-Hipopneas del Sueño (SAHS). El SAHS es el trastorno más común de aquellos que afectan al sueño. Afecta a un rango del 3% al 7% de la población con consecuencias severas sobre la salud. El diagnóstico requiere la realización de un análisis polisomnográfico (PSG) en una Unidad del Sueño de un centro hospitalario. El análisis manual de dicha prueba resulta muy costoso en tiempo y esfuerzo para el médico especialista, y como consecuencia en un elevado coste económico. El sistema desarrollado lleva a cabo el análisis automático del PSG desde una perspectiva integral. A tal efecto, primero se realiza un análisis de las señales neurofisiológicas vinculadas al sueño para obtener el hipnograma, y seguidamente, se lleva a cabo un análisis neumológico de las señales respiratorias interpretándolas en el contexto que marcan las demás señales del PSG. Para lleva a cabo dicha tarea el sistema se apoya en el uso de distintas técnicas de inteligencia artificial, con especial atención al uso mecanismos de razonamiento con soporte a la imprecisión. El principal objetivo del sistema propuesto es la mejora del procedimiento diagnóstico y ayudar a los médicos en diagnóstico del SAHS.[Resumo] Esta tese aborda o desenvolvemento dun sistema intelixente de apoio á decisión clínica para o diagnóstico do Síndrome de Apneas-Hipopneas do Sono (SAHS). O SAHS é o trastorno máis común daqueles que afectan ao sono. Afecta a un rango do 3% ao 7% da poboación con consecuencias severas sobre a saúde. O diagnóstico pasa pola realización dunha análise polisomnográfica (PSG) nunha Unidade do Sono dun centro hospitalario. A análise manual da devandita proba resulta moi custosa en tempo e esforzo para o médico especialista, e como consecuencia nun elevado custo económico. O sistema desenvolvido leva a cabo a análise automática do PSG dende unha perspectiva integral. A tal efecto, primeiro realizase unha análise dos sinais neurofisiolóxicos vinculados ao sono para obter o hipnograma, e seguidamente, lévase a cabo unha análise neumolóxica dos sinais respiratorios interpretándoos no contexto que marcan os demais sinais do PSG. Para leva a cabo esta tarefa o sistema apoiarase no uso de distintas técnicas de intelixencia artificial, con especial atención a mecanismos de razoamento con soporte para a imprecisión. O principal obxectivo do sistema proposto é a mellora do procedemento diagnóstico e axudar aos médicos no diagnóstico do SAHS