Currently, sleep staging is accomplished is by clinical polysomnography (PSG). By extracting features from different combinations of electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals, this study uses neural network to perform sleep staging. A whole night and complete sleep stage contains wake stage, rapid eye movement (REM) stage, stage 1, stage 2, and slow wave sleep (SWS) stage. This project focuses on the classification of wake stage and light-sleep (stage 1 and 2). These three stages are classified by a two-step process. At first, wake stage and light-sleep are divided into two parts. Second, light sleep is divided into stage 1 and stage 2. For a fixed number of channels, this work identifies the best combination of signal channels. In addition, by simultaneously considering the Neighboring epochs Rule classifier, this work also introduces an empirical rule to improve the classification accuracy. Among the tested databases which contain two Medicine center and sixteen sets of different signal channels, the best results are obtained from the group of patients with the low average RDI value. They include the group that has a mean 15% SWS and the group that uses CPAP. As a whole, the combinative features of four channels are better results of classification. For our best results, the sensitivity and PPV of wake and stage 2 varies from 85%~88%, and those of stage 1 are respective 44.84% and 53.61%. And the total classification of sleep staging is 84.59%. Apparently, the research has satisfactory results on sleep staging. Keywords: Sleep Medicine, Sleep stage, Neural Networks

Obstructive sleep apnoea (OSA) is a common condition characterized by recurrent complete and partial upper airway obstruction. OSA sufferers have been shown to have a significantly smaller upper airway lumen compared to non-OSA sufferers. However, non-anatomical factors of sleep stage, arousability and neuromechanical responses to airway occlusion and chemosensitivity are likely to play a significant part in influencing OSA severity across the night. An exploration of these non-anatomical factors forms the basis for the experiments in this thesis. In the first experimental chapter presented in this thesis, a detailed retrospective epoch by epoch analysis of nocturnal polysomnography in 253 patients referred to a clinical sleep service was performed to examine differences in sleep apnoea severity and arousal indices across the different stages of sleep, while controlling for posture. Both patients with and without OSA demonstrated significant reductions in respiratory and arousal event frequencies from stage 1 to 4 with intermediate frequencies in REM sleep. Lateral posture was also associated with significant improvements in OSA and arousal frequencies, with an effect size comparable to that of sleep stage. The majority of patients showed significant reductions in OSA severity during slow wave sleep. In non-REM sleep, there was a strong correlation between OSA severity and arousal frequency. These results confirm in a large group of patients, a strong sleep stage dependence of both OSA and arousal frequencies. The second study in this thesis explores the development of a CO2 stabilising or ‘clamp’ device to enable the provision of positive airway pressure, and by proportional rebreathing, the maintenance of relatively constant end-tidal CO2 despite significant hyperventilation. Healthy volunteers performed brief periods of significant voluntary hyperventilation at 2 levels of CPAP with the rebreathing function off and with active CO2 clamping in randomized order. Compared to CPAP alone, the device substantially attenuated hypocapnia associated with hyperventilation. The third study of the thesis was designed to investigate if increasing and stabilizing end-tidal CO2 could improve obstructive breathing patterns during sleep. 10 patients with severe OSA underwent rapid CPAP dialdown from therapeutic to a sub-therapeutic level to experimentally induce acute, partial upper airway obstruction over 2 minute periods repeated throughout the night. The CO2 clamp device developed and validated in Study 2 was used to determine whether during periods of partial upper airway obstruction with severe flow limitation, (1) increased end-tidal CO2 resulted in improved airflow and ventilation and (2) clamping end-tidal CO2 lessened post-arousal ventilatory undershoot. Three conditions were studied in random order: no clamping of CO2, clamping of end-tidal CO2 3-4 mmHg above eucapnic levels during the pre-dialdown baseline period only, and clamping of CO2 above eucapnia during both baseline and dialdown periods. Elevated CO2 in the baseline period alone or in the baseline and dialdown periods together resulted in significantly higher peak inspiratory flows and ventilation compared to the no clamp condition. Breath-by-breath analysis immediately pre- and post-arousal showed higher end-tidal CO2 despite hyperventilation immediately post-arousal and attenuation of ventilatory undershoot in CO2 versus non-CO2 clamped conditions. These results support that modulation of ventilatory drive by changes in pre- and post-arousal CO2 are likely to importantly influence upper airway and ventilatory stability in OSA. The fourth study was designed to explore several possible pathophysiological mechanisms whereby obstructive sleep apnoea is improved in stages 3 & 4 (slow wave) versus stage 2 sleep. 10 patients with severe OSA who demonstrated significant reductions in OSA frequency during slow wave sleep on diagnostic investigation were studied. Patients underwent rapid dialdowns from therapeutic CPAP to 3 different pre-determined sub-therapeutic pressures to induce partial airway obstruction and complete airway occlusions in a randomised sequence during the night in both stage 2 and slow wave sleep. Partial airway obstructions and complete occlusions were maintained until arousal occurred or until 2 minutes had elapsed, whichever came first. After airway occlusions, time to arousal, peak pre-arousal negative epiglottic pressure and the rate of ventilatory drive augmentation were significantly greater, suggesting a higher arousal threshold and ventilatory responsiveness to respiratory stimuli during slow wave compared to stage 2 sleep. Post dialdowns, the likelihood of arousal was lower with less severe dialdowns and in slow wave compared to stage 2 sleep. Respiratory drive measured by epiglottic pressure progressively increased post-dialdown, but did not translate into increases in peak flow or ventilation pre-arousal and was not different between sleep stages. These data suggest that while arousal time and propensity following respiratory challenge are altered by sleep depth, there is little evidence to support that upper airway and ventilatory compensation responses to respiratory load are fundamentally improved in slow wave compared to stage 2 sleep. In summary, sleep stage, arousal threshold and chemical drive appear to strongly influence upper airway and ventilatory stability in OSA and are suggestive of important non-anatomical pathogenic mechanisms in OSA.

The use of Electroencephalograms (EEG) are essential to the analysis of sleep disorders in patients. With the use of electroencephalograms, electro-oculograms (EOG), and electromyograms (EMG), doctors and EEG technician can make conclusions about the sleep patterns of patients. In particular, the classification of the sleep data into various stages, such as NREM I-IV, REM, Awake, is extremely important. The EEG signal itself is highly sensitive to physiological and non-physiological artifacts. Trained human experts can accommodate for these artifacts while they are analyzing the EEG signal. <br /><br /> However, if some of these artifacts are removed prior to analysis, their job will be become easier. Furthermore, one of the biggest motivations, of our team's research is the construction of a portable device that can analyze the sleep data as they are being collected. For this task, the sleep data must be analyzed completely automatically in order to make the classifications. <br /><br /> The research presented in this thesis concerns itself with the <em>denoising</em> and the <em>feature selection</em> aspects of the teams' goals. Since humans are able to process artifacts and ignore them prior to classification, an automated system should have the same capabilities or close to them. As such, the denoising step is performed to condition the data prior to any other stages of the sleep stage neoclassicisms. As mentioned before, the denoising step, by itself, is useful to human EEG technicians as well. <br /><br /> The denoising step in this research mainly looks at EOG artifacts and artifacts isolated to a single EEG channel, such as electrode pop artifacts. The first two algorithms uses Wavelets exclusively (BWDA and WDA), while the third algorithm is a mixture of Wavelets and In- dependent Component Analysis (IDA). With the BWDA algorithm, determining <em>consistent</em> thresholds proved to be a difficult task. With the WDA algorithm, the performance was better, since the selection of the thresholds was more straight-forward and since there was more control over defining the duration of the artifacts. The IDA algorithm performed inferior to the WDA algorithm. This could have been due to the small number of measurement channels or the automated sub-classifier used to select the <em>denoised EEG signal</em> from the set of ICA <em>demixed</em> signals. <br /><br /> The feature selection stage is extremely important as it selects the most pertinent features to make a particular classification. Without such a step, the classifier will have to process useless data, which might result in a poorer classification. Furthermore, unnecessary features will take up valuable computer cycles as well. In a portable device, due to battery consumption, wasting computer cycles is not an option. The research presented in this thesis shows the importance of a systematic feature selection step in EEG classification. The feature selection step produced excellent results with a maximum use of just 5 features. During automated classification, this is extremely important as the automated classifier will only have to calculate 5 features for each given epoch.

Electro-oculography signals are corneo-retinal potentials that carry informationpertaining to eye movements. This information can be used to estimate drowsinesslevel of the subject which could provide interesting insights into research of acci-dent prevention. Of all features present, blink duration has been proved to be aneffective measure of drowsiness. The aim of this thesis work is to build a portablesystem to acquire and analyze electro-oculographic (EOG) signals in real-time.The system contains two sub-systems; a hardware sub-system that consists of thefilters, amplifiers, data acquisition card and isolation and the software sub-systemthat contains the program to acquire and analyze the signal and present the resultsto the observer. The filters were designed starting with simulation, implementa-tion on the prototype board, culminating in the design of a printed circuit board(PCB) and packaging. The complete software was written in PythonTMusing sev-eral relevant libraries for data processing. A text-based user interface was createdto enable easy user interaction. The results are graphically displayed in real-time. Ex-situ tests were done with two volunteers while in-situ test was done onone subject. The data from the in-situ tests showed "good signal quality" in a"noisy" environment concurring with the design specifications. To motivate theimportance of calibration, two calibration paradigms were used during ex-situtests, where one paradigm records only normal blinks while the other records longblinks and the results showed differences in detection and error rates. The obser-vations made from performance tests at various levels gave "satisfactory results"and proved the usefulness of the system for experimental purposes in-situ.

The use of Electroencephalograms (EEG) are essential to the analysis of sleep disorders in patients. With the use of electroencephalograms, electro-oculograms (EOG), and electromyograms (EMG), doctors and EEG technician can make conclusions about the sleep patterns of patients. In particular, the classification of the sleep data into various stages, such as NREM I-IV, REM, Awake, is extremely important. The EEG signal itself is highly sensitive to physiological and non-physiological artifacts. Trained human experts can accommodate for these artifacts while they are analyzing the EEG signal. <br /><br /> However, if some of these artifacts are removed prior to analysis, their job will be become easier. Furthermore, one of the biggest motivations, of our team's research is the construction of a portable device that can analyze the sleep data as they are being collected. For this task, the sleep data must be analyzed completely automatically in order to make the classifications. <br /><br /> The research presented in this thesis concerns itself with the <em>denoising</em> and the <em>feature selection</em> aspects of the teams' goals. Since humans are able to process artifacts and ignore them prior to classification, an automated system should have the same capabilities or close to them. As such, the denoising step is performed to condition the data prior to any other stages of the sleep stage neoclassicisms. As mentioned before, the denoising step, by itself, is useful to human EEG technicians as well. <br /><br /> The denoising step in this research mainly looks at EOG artifacts and artifacts isolated to a single EEG channel, such as electrode pop artifacts. The first two algorithms uses Wavelets exclusively (BWDA and WDA), while the third algorithm is a mixture of Wavelets and In- dependent Component Analysis (IDA). With the BWDA algorithm, determining <em>consistent</em> thresholds proved to be a difficult task. With the WDA algorithm, the performance was better, since the selection of the thresholds was more straight-forward and since there was more control over defining the duration of the artifacts. The IDA algorithm performed inferior to the WDA algorithm. This could have been due to the small number of measurement channels or the automated sub-classifier used to select the <em>denoised EEG signal</em> from the set of ICA <em>demixed</em> signals. <br /><br /> The feature selection stage is extremely important as it selects the most pertinent features to make a particular classification. Without such a step, the classifier will have to process useless data, which might result in a poorer classification. Furthermore, unnecessary features will take up valuable computer cycles as well. In a portable device, due to battery consumption, wasting computer cycles is not an option. The research presented in this thesis shows the importance of a systematic feature selection step in EEG classification. The feature selection step produced excellent results with a maximum use of just 5 features. During automated classification, this is extremely important as the automated classifier will only have to calculate 5 features for each given epoch.

[ES] Las redes de sensores inalámbricas (WSN) han experimentado un resurgimiento debido al desarrollo de la Internet de las Cosas (IoT). Una de las características de las aplicaciones de la IoT es la necesidad de hacer uso de dispositivos sensores y actuadores. En aplicaciones como automatización de edificios, de gestión energética, industriales o de salud, los nodos sensores que componen la WSN, transmiten información a un colector central o sink. La información es posteriormente procesada, analizada y utilizada para propósitos específicos. En cada una de estas aplicaciones, los dispositivos sensores pueden considerarse como parte de una WSN. En ese sentido el modelado y la evaluación de las prestaciones en las WSN es importante, ya que permite obtener una visión más clara de su comportamiento, facilitando un adecuado diseño y una exitosa puesta en operación. En el presente trabajo de tesis se han desarrollado modelos matemáticos para evaluar las prestaciones de WSN, los cuales están basados en Cadenas de Markov en Tiempo Discreto (DTMC). Los parámetros de prestaciones elegidos para la evaluación son: energía consumida promedio, eficiencia energética, caudal cursado y retardo promedio de los paquetes. Los resultados que se han obtenido han sido validados por medio de simulación basada en eventos discretos (DES). Existen estudios de WSN en escenarios homogéneos, donde los nodos que componen la red inalámbrica son del mismo tipo y tienen las mismas características de operación. En estos análisis se definen WSN homogéneas compuestas por un nodo central o sumidero (sink), que recibe la información de los nodos sensores localizados alrededor, formando una célula o cluster. Estos nodos realizan las transmisiones en SPT (Single Packet Transmission), enviando un solo paquete por ciclo de transmisión. Sin embargo, es posible encontrar, más ahora con el desarrollo de la IoT, escenarios donde coexisten distintos tipos de nodos, con características diferentes y, por tanto, con requerimientos de operación específicos. Esto da lugar a la formación de clusters cuyos nodos tienen aplicaciones distintas, desigual consumo de energía, diversas tasas de trasmisión de datos, e incluso diferentes prioridades de acceso al canal de transmisión. Este tipo de escenarios, que denominamos heterogéneos, forman parte de los escenarios estudiados en el presente trabajo de tesis. En una primera parte, se ha desarrollado un modelo para evaluar las prestaciones de una WSN heterogénea y con prioridades de acceso al medio. El modelado incluye un par de DTMC de dos dimensiones (2D-DTMC) cada una, cuya solución en términos de la distribución de probabilidad estacionaria, es utilizada para determinar los parámetros de prestaciones. Se desarrollan, por tanto, expresiones cerradas para los parámetros de prestaciones, en función de la distribución estacionaria que se ha obtenido a partir de la solución de las 2D-DTMC. En una segunda parte, se desarrolla un modelo analítico también pensado para escenarios heterogéneos y con prioridades, pero en el que los nodos de la WSN, cuando consiguen acceso al canal, transmiten un conjunto de paquetes en vez de uno solo como en el modelo de la primera parte. Estos dos modos de operación de los sensores los denominamos aggregated packet trans- mission (APT) y single packet transmission (SPT), respectivamente. El número de paquetes que un nodo funcionando en APT trasmite cuando accede al canal es el menor entre un parámetro configurable y el número de paquetes que tuviera en la cola en ese momento. Este modo de operación consigue una mayor eficiencia energética y un aumento en el caudal cursado, además de una disminución en el retardo promedio de los paquetes. En una tercera parte, se propone un nuevo procedimiento analítico para la determinación del consumo energético de los nodos que conforman una WSN. A diferencia de los métodos de cálculo anteriores, la nueva prop / [CA] Les xarxes de sensors sense fils (WSN) han experimentat un ressorgiment causa de al desenvolupament de la Internet de les Coses (IoT). Una de les característiques de IoT és la inclusió, en les seves aplicacions, de dispositius sensors i actuadors. En aplicacions com automatització d'edificis, de gestió energètica, industrials o de salut, els nodes sensors que componen la WSN, transmeten informació a un col·lector central o sink. La informació és posteriorment processada, analitzada i utilitzada per a propòsits específics. En cadascuna d'aquestes aplicacions, els dispositius sensors poden considerar com a part d'una WSN. En aquest sentit el modelitzat i l'avaluació de l'acompliment en les WSN és important, ja que permet obtenir una visió més clara del seu comportament, facilitant un adequat disseny i una exitosa posada en operació. En el present treball de tesi s'han desenvolupat models matemàtics per avaluar l'acompliment de WSN, els quals estan basats en Cadenes de Markov en Temps Discret (DTMC). Els paràmetres d'acompliment obtinguts per a l'avaluació són: energia consumida mitjana, eficiència energètica, cabal cursat i retard mitjà dels paquets. Els resultats que s'han obtingut, han estat validats per mitjà de simulació basada en esdeveniments discrets (DES). Existeixen estudis de WSN en escenaris homogenis, on els nodes que componen la xarxa sense fils són de el mateix tipus i tenen les mateixes característiques d'operació. En aquests anàlisis prèvies es defineixen WSN homogènies compostes per un node central o embornal (sink), que rep la informació dels nodes sensors localitzats al voltant, formant una cèl·lula o cluster. Aquests nodes realitzen les transmissió en SPT (Single Packet Transmission), és a dir, enviant un sol paquet cada vegada que transmeten. No obstant això, és possible trobar, més ara amb el desenvolupament de la IOT, escenaris on hi ha una coexistència de distints tipus de nodes, amb característiques diferents i, per tant, amb requeriments d'operació específics. Això dona lloc a formació de clusters els nodes tenen aplicacions diferents, desigual consum d'energia, diverses taxes de transmissió de dades, i fins i tot diferent prioritats d'accés a canal de transmissió. Aquest tipus d'escenaris, que anomenem heterogenis, formen part dels escenaris estudiats en el present treball de tesi. En una primera part, s'ha desenvolupat un model per avaluar l'acompliment d'una WSN heterogènia i amb prioritats d'accés al medi. El modelitzat inclou un parell DTMC de dues dimensions (2D-DTMC), la solució en termes de la distribució estacionària de probabilitat, és utilitzada per obtenir posteriorment els paràmetres d'acompliment. Es desenvolupen, per tant, expressions tancades per a la determinació dels paràmetres d'acompliment, on és substituïda la distribució estacionària que s'ha obtingut a partir de la solució de les 2D-DTMC. En una segona part, es desenvolupa un model, en el qual els nodes pertanyents a la WSN, poden transmetre els seus paquets en agregat (APT) en escenaris heterogenis i amb prioritats. A diferència del model anterior, on els nodes transmeten un paquet per cicle (SPT), en APT els nodes poden transmetre més d'un paquet. Això porta com a conseqüència una major eficiència energètica, a més d'un augment en el cabal cursat i disminució en el retard mitjana. En una tercera part, es proposa un nou desenvolupament analític per a la determinació del consum energètic dels nodes que conformen una WSN. A diferència de les expressions utilitzades anteriorment per al càlcul del consum energètic, aquesta proposta alternativa permet obtenir resultats més precisos a través del desenvolupament d'expressions més intuïtives i sistemàtiques. Amb aquest nou procediment, es realitzen estudis energètics per WSN en escenaris homogenis i heterogenis. / [EN] Wireless sensor networks (WSN) have experienced a resurgence due to the development of the Internet of Things (IoT). One of the characteristics of IoT is the deployment of applications that require sensor devices and actuators. In applications such as building automation, energy management, industrial or health, the sensor nodes that make up the WSN transmit information to a central collector or sink. The information is processed, analyzed, and used for specific purposes. In each of these applications, the sensor devices can be considered part of a WSN. In this sense, the modeling and performance evaluation of WSN is important, since it allows obtaining a clearer vision of their behavior, facilitating an adequate design and a successful operation. In the present thesis, analytical models based on Discrete Time Markov Chains (DTMC) have been developed to evaluate the performance of WSN. The parameters defined for the performance evaluation are: average consumed energy, energy efficiency, throughput and average packet delay. The obtained results have been validated by means of discrete event simulation (DES). There are studies of WSN in homogeneous scenarios, where the nodes that compose the WSN are of the same type and have the same operating characteristics. In these previous studies, homogeneous WSN are defined as a cell or cluster composed of a central node or sink, which receives the information from the sensor nodes located around it. These nodes operate in SPT (Single Packet Transmission), sending a single packet per transmission cycle. However, it is possible to find, especially now with the development of the IoT, scenarios where different types of nodes coexist, although they have different characteristics or specific operational requirements. This results in the formation of clusters whose nodes have different applications, uneven power consumption, different data transmission rates, and even different priorities for access to the transmission channel. These types of scenarios, which we call heterogeneous, are part of the scenarios studied in this thesis work. In the first part, a model has been developed to evaluate the performance of a heterogeneous WSN and with priorities to access a common channel. The model includes a two-dimensional DTMC pair (2D-DTMC), whose solution in terms of the stationary probability distribution is used to obtain the performance parameters. Closed expressions are provided for the determination of performance parameters of interest, given in terms of the stationary distribution of the 2D-DTMC. In a second part, an analytical model is developed to evaluate the performance of a heterogeneous WSN, where nodes operate in aggregate packet transmission (APT) mode and deploy different channel access priorities. Un like the previous model, where the nodes transmit one packet per cycle (SPT) when they gain access to the channel, in APT the nodes can transmit a number of packets larger than one, that is the minimum between a configurable parameter and the number of packets in the packet queue of the node. This results in greater energy efficiency and throughput, while decreases the average packet delay. In a third part, a new analytical model is proposed to determine the energy consumption of the nodes that make up a WSN. Unlike previous computation procedures, this alternative proposal is based on more intuitive and systematic expressions and allows to obtain more accurate results. With this new procedure, energy studies are performed for WSN in homogeneous and heterogeneous scenarios. / Este trabajo se ha desarrollado en el marco de los siguientes proyectos de investigación: Platform of Services for Smart Cities with Dense Machine to Machine Networks, PLASMA, TIN2013-47272-C2-1-R and New Paradigms of Elastic Networks for a World Radically Based on Cloud and Fog Computing, Elastic Networks, TEC2015-71932-REDT. También quisiera agradecer el apoyo recibido por parte de the European Union under the program Erasmus Mundus Partnerships, project EuroinkaNet, GRANT AGREEMENT NUMBER - 2014-0870/001/001 y La Secretaria de Educación Pública (México), bajo el Programa para el Desarrollo Profesional Docente: SEP-SES (DSA/103.5/15/6629). / Portillo Jiménez, C. (2021). Modelado y evaluación de prestaciones de redes de sensores inalámbricos heterogéneos con ciclo de trabajo síncrono [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171275 / TESIS