Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

[pt] Em diversos problemas encontrados na literatura, se faz necessária alguma coordenação entre os agentes para que a tarefa seja realizada de forma ótima. Entretanto, pode ser difícil a obtenção desta coordenação por conta da quantidade e características dos agentes, dinâmica do ambiente e/ou complexidade da tarefa. O objetivo principal deste estudo é propor um modelo que possa se adaptar a problemas heterogêneos de coordenação e de dimensões elevadas, com aprendizado autônomo e que tenha convergência satisfatória, o qual foi denominado Modelo de Neuro Co-Evolução com Inspiração Quântica (NCoQ). O modelo se utiliza dos paradigmas da física quântica e da co-evolução biológica, evoluindo concomitantemente sub-populações de indivíduos quânticos para obter ganhos de convergência. A representação dos indivíduos por pulsos quânticos consegue reduzir o número de indivíduos em cada população, além de ser a mais recomendada para a utilização de neuro-evolução por conta da representação real. Ressalta-se também a capacidade do modelo em obter de forma autônoma a melhor configuração de arquitetura para as redes neurais de cada agente, não exigindo do programador a escolha deste parâmetro. Foram propostos novos operadores quânticos de crossover e mutação que foram comparados na otimização de funções de diversas dimensões. Para testar o desempenho do modelo, foram desenvolvidas, em linguagem MATLAB, simulações para o problema presa predador, para o benchmark multi-rover de exploração de ambientes e uma simulação para cobertura telefônica. Foram feitas comparações com outros modelos neuro-evolutivos encontrados na literatura, tendo o modelo NCoQ apresentado os melhores resultados. / [en] Many problems in the literature require some coordination among agents so a specific task can be executed more efficiently. However, this coordination can be difficult because of the quantity and characteristics of the agents, environment dynamics and/or task complexity. The main contribution of this Thesis is the proposal of a model, called Quantum Inspired Neuro Co-Evolution (NCoQ), that can adapt to heterogeneous multi-agent problems in high dimensions utilizing self-learning and that has satisfactory convergence. The model is inspired in quantum physics and biological co-evolution paradigms and evolves concomitantly subpopulations of quantum individuals to get convergence gains. The representation of individuals for quantum functions is able to reduce the numbers of individuals in each population and it is the most recommended for real neuro-evolution representation. It s also important to point out the model capacity in self-finding the best architecture of the neural networks agents, not requiring an a priori definition of this parameter. New crossover and mutation quantum operators were also proposed and compared in functions optimization of multiple dimensions. To test the model performance, three MATLAB simulations were developed: prey-predator task, multi-rover task and cell phone coverage area simulation. Comparisons were made against others neuro-evolution models found in literature and the NCoQ model attained the best results.

The objective of this thesis is to investigate the flow conditions that lead to coarse grain entrainment at near incipient motion conditions. Herein, a new conceptual approach is proposed, which in addition to the magnitude of hydrodynamic force or flow power, takes into account the duration of the flow event. Two criteria for inception of grain entrainment, namely the critical impulse and critical energy concepts, are proposed and compared. These frameworks adopt a force or energy perspective, considering the momentum or energy transfer from each flow event to the particle respectively, to describe the phenomenon. A series of conducted mobile particle experiments, are analyzed to examine the validity of the proposed approaches. First a set of bench-top experiments incorporates an electromagnet which applies pulses of known magnitude and duration to a steel spherical particle in a controlled fashion, so as to identify the critical level for entrainment. The utility of the above criteria is also demonstrated for the case of entrainment by the action of turbulent flow, via analysis of a series of flume experiments, where both the history of hydrodynamic forces exerted on the particle as well as its response are recorded simultaneously. Statistical modeling of the distribution of impulses, as well as conditional excess impulses, is performed using distributions from Extreme Value Theory to effectively model the episodic nature of the occurrence of these events. For the examined uniform and low mobility flow conditions, a power law relationship is proposed for describing the magnitude and frequency of occurrence of the impulse events. The Weibull and exponential distributions provide a good fit for the time between particle entrainments. In addition to these statistical tools, a number of Adaptive Neuro-Fuzzy Inference Systems employing different input representations are used to learn the nonlinear dynamics of the system and perform statistical prediction. The performance of these models is assessed in terms of their broad validity, efficiency and forecast accuracy. Even though the impulse and energy criteria are deeply interrelated, the latter is shown to be advantageous with regard to its performance, applicability and extension ability. The effect of single or multiple highly energetic events carried by certain coherent flow structures (mainly strong sweep events) with regard to the particle response is also investigated. / Ph. D.

Modern autonomous systems should be able to interact with their surroundings in a flexible yet safe manner. To guarantee this behavior, such systems must learn how to approach unseen entities in their environment through the inference of relationships between actions and objects, called affordances. This research project introduces a neuro-symbolic AI system capable of inferring affordances using attribute detection and knowledge representation as its core principles. The attribute detection module employs a visuo-lingual image captioning model to extract the key object attributes of a scene, while the cognitive knowledge module infers the affordances of those attributes using conditional probability. The practical capabilities of the neuro-symbolic AI system are assessed by implementing a simulated robot system that interacts within the problem space of jars and bottles. The neuro-symbolic AI system is evaluated through its caption-inferring capabilities using image captioning and machine translation metrics. The scores registered in the evaluation show a successful attribute captioning rate of more than 71%. The robot simulation is evaluated within a Unity virtual environment by interacting with 50 jars and bottles, equally divided between lifting and twisting affordances. The robot system successfully interacts with all the objects in the scene due to the robustness of the architecture but fails in the inference process 24 out of the 50 iterations. Contrary to previous works approaching the problem as a classification task, this study shows that affordance inference can be successfully implemented using a cognitive visuo-lingual method. The study’s results justify further study into the use of neuro-symbolic AI approaches to affordance inference.

Today engineering companies expend a large amount of resources on the detection and correction of the bugs (defects) in their software. These bugs are usually due to errors and mistakes made by programmers while writing the code or writing the specifications. No tool is able to detect all of these bugs. Some of these bugs remain undetected despite testing of the code. For these reasons, many researchers have tried to find indicators in the software’s source codes that can be used to predict the presence of bugs. Every bug in the source code is a potentially failure of the program to perform as expected. Therefore, programs are tested with many different cases in an attempt to cover all the possible paths through the program to detect all of these bugs. Early prediction of bugs informs the programmers about the location of the bugs in the code. Thus, programmers can more carefully test the more error prone files, and thus save a lot of time by not testing error free files. This thesis project created a tool that is able to predict error prone source code written in C++. In order to achieve this, we have utilized one predictor which has been extremely well studied: software metrics. Many studies have demonstrated that there is a relationship between software metrics and the presence of bugs. In this project a Neuro-Fuzzy hybrid model based on Fuzzy c-means and Radial Basis Neural Network has been used. The efficiency of the model has been tested in a software project at Ericsson. Testing of this model proved that the program does not achieve high accuracy due to the lack of independent samples in the data set. However, experiments did show that classification models provide better predictions than regression models. The thesis concluded by suggesting future work that could improve the performance of this program. / Idag spenderar ingenjörsföretag en stor mängd resurser på att upptäcka och korrigera buggar (fel) i sin mjukvara. Det är oftast programmerare som inför dessa buggar på grund av fel och misstag som uppkommer när de skriver koden eller specifikationerna. Inget verktyg kan detektera alla dessa buggar. Några av buggarna förblir oupptäckta trots testning av koden. Av dessa skäl har många forskare försökt hitta indikatorer i programvarans källkod som kan användas för att förutsäga förekomsten av buggar. Varje fel i källkoden är ett potentiellt misslyckande som gör att applikationen inte fungerar som förväntat. För att hitta buggarna testas koden med många olika testfall för att försöka täcka alla möjliga kombinationer och fall. Förutsägelse av buggar informerar programmerarna om var i koden buggarna finns. Således kan programmerarna mer noggrant testa felbenägna filer och därmed spara mycket tid genom att inte behöva testa felfria filer. Detta examensarbete har skapat ett verktyg som kan förutsäga felbenägen källkod skriven i C ++. För att uppnå detta har vi utnyttjat en välkänd metod som heter Software Metrics. Många studier har visat att det finns ett samband mellan Software Metrics och förekomsten av buggar. I detta projekt har en Neuro-Fuzzy hybridmodell baserad på Fuzzy c-means och Radial Basis Neural Network använts. Effektiviteten av modellen har testats i ett mjukvaruprojekt på Ericsson. Testning av denna modell visade att programmet inte Uppnå hög noggrannhet på grund av bristen av oberoende urval i datauppsättningen. Men gjordt experiment visade att klassificering modeller ger bättre förutsägelser än regressionsmodeller. Exjobbet avslutade genom att föreslå framtida arbetet som skulle kunna förbättra detta program. / Actualmente las empresas de ingeniería derivan una gran cantidad de recursos a la detección y corrección de errores en sus códigos software. Estos errores se deben generalmente a los errores cometidos por los desarrolladores cuando escriben el código o sus especificaciones.  No hay ninguna herramienta capaz de detectar todos estos errores y algunos de ellos pasan desapercibidos tras el proceso de pruebas. Por esta razón, numerosas investigaciones han intentado encontrar indicadores en los códigos fuente del software que puedan ser utilizados para detectar la presencia de errores. Cada error en un código fuente es un error potencial en el funcionamiento del programa, por ello los programas son sometidos a exhaustivas pruebas que cubren (o intentan cubrir) todos los posibles caminos del programa para detectar todos sus errores. La temprana localización de errores informa a los programadores dedicados a la realización de estas pruebas sobre la ubicación de estos errores en el código. Así, los programadores pueden probar con más cuidado los archivos más propensos a tener errores dejando a un lado los archivos libres de error. En este proyecto se ha creado una herramienta capaz de predecir código software propenso a errores escrito en C++. Para ello, en este proyecto se ha utilizado un indicador que ha sido cuidadosamente estudiado y ha demostrado su relación con la presencia de errores: las métricas del software. En este proyecto un modelo híbrido neuro-disfuso basado en Fuzzy c-means y en redes neuronales de función de base radial ha sido utilizado. La eficacia de este modelo ha sido probada en un proyecto software de Ericsson. Como resultado se ha comprobado que el modelo no alcanza una alta precisión debido a la falta de muestras independientes en el conjunto de datos y los experimentos han mostrado que los modelos de clasificación proporcionan mejores predicciones que los modelos de regresión. El proyecto concluye sugiriendo trabajo que mejoraría el funcionamiento del programa en el futuro.

Motivation - Depression is a mental disorder that has been associated with cardiovascular morbidity and mortality in the Western world. Cardiometablic mechanisms have been implicated as possible intermediating factors in the relationship between depressive symptoms and cardiovascular disease; however this has not yet been determined in black Africans (hereafter referred to as Africans). Aim - The overarching aim of this study was to investigate the relationship between depressive symptoms and cardiometabolic risk. We therefore aimed to assess cardiometabolic function, neuroendocrine responses, inflammatory and haemostatic markers in Africans with depressive symptoms compared to those without symptoms of depression. Methodology - Manuscripts presented in Chapter 2, 3 and 4 utilised data from the cross-sectional, target population multi-disciplinary “Sympathetic activity and Ambulatory Blood Pressure in Africans” (SABPA) study. The participants comprised of 200 African teachers from the Dr Kenneth Kaunda District in North-West province, South Africa. As cardiovascular disease is compromised by a positive HIV status, 19 participants were excluded from further statistical analysis. Stratification was based on the Patient Health Questionnaire 9-item (PHQ-9), which has been validated in a sub-Saharan African setting. PHQ-9 scores > 10 were used to classify participants as having signs of depressive symptoms. Subjects were further stratified by gender (Manuscript 1 and 3) and cortisol responses (Manuscript 2). Cardiometabolic health measures included 24-hour blood pressure, metabolic syndrome markers, neuroendocrine markers [cortisol and 3-methoxy-4-hydroxy-phenylglycol (MHPG)], left ventricular hypertrophy (LVH),inflammatory and haemostatic markers (fibrinogen, C-reactive protein, plasminogen activator inhibitor-1 and D-dimer). Resting 12-lead ECG Cornell Product-Left ventricular hypertrophy (CP-LVH) was measured as a marker of target end-organ damage and cardiovascular dysfunction (Manuscript 1 and 2). Means and prevalence were computed through t-test and Chi-square analysis respectively. Significant differences of mean cardiometabolic measures between depressive symptom status groups were also determined by analysis of covariance (adjusted for traditional cardiovascular risk factors and additional factors as specific per manuscript). Multivariate analysis was used to demonstrate associations between left ventricular hypertrophy (LVH) and cardiometabolic markers in Africans with depressive symptoms (Manuscript 1 and 2) and a logistic regression analysis were performed to examine the association between depressive symptoms and inflammatory/haemostatic factors (Manuscript 3). All subjects who participated gave informed consent, the study was approved by the Ethics Committee of North-West University (NWU-0003607S6), in accordance with the principles outlined by the World Medical Association Declaration of Helsinki of 1975 (revised 2008). Results and conclusions of the individual manuscripts - The aim of the study was to investigate the associations between depressive symptoms and cardiometabolic function including cardiovascular dysfunction. Markers of cardiometabolic function assessed were 24 hour blood pressure measurements, metabolic syndrome markers, neuroendocrine markers [cortisol and 3-methoxy-4-hydroxy-phenylglycol (MHPG)], inflammatory and haemostatic variables (fibrinogen, C-reactive protein, plasminogen activator inhibitor-1 and D-dimer). Manuscript 1, focused on LVH as a marker of cardiovascular dysfunction and metabolic syndrome components as markers of cardiometabolic function. The aim of the study was to assess the associations between LVH and metabolic syndrome (MetS) risk markers in participants with and without depressive symptoms. Results revealed that in African men with depressive symptoms the most significant determinants of LVH were systolic blood pressure (SBP) and the percentage glycosylated haemoglobin (HbA1c). While in African women (with depressive symptoms), this association was determined by low high-density lipoprotein (HDL-cholesterol). The study concluded that in black African men, independent of depressive symptoms, cardiometabolic factors (namely SBP and HbA1c) may be the driving significant factors in the development of cardiovascular diseases. Furthermore, the data showed that depressive symptoms in African women were associated with a measure of target end organ damage, and that this association was driven by a metabolic factor. Manuscript 2, the aim of this manuscript was to examine the relationship between depressive symptoms, neuroendocrine responses [with cortisol and 3-methoxy-phenylglycol (MHPG) as markers] and cardiovascular risk, i.e. LVH. The results revealed that Africans with depressive symptoms demonstrated blunted cortisol and MHPG levels in response to acute mental stress, in comparison to those without symptoms of depression. Additionally, these low cortisol and blunted MHPG responses were associated with LVH in this ethnic group. The conclusion for this manuscript was that, blunted neuroendocrine responses linked depressive symptoms and ECG left ventricular hypertrophy in Africans. When coupled to their hypertensive status, these vasoconstrictive responses (cortisol and MHPG) may underpin the increased long-term depression and vascular disease risk in urban Africans. Manuscript 3, the aim of this manuscript was to investigate the relationship between depressive symptoms and inflammatory/haemostatic markers in a cohort of urban-dwelling black African men and women. Our data demonstrated hypercoagulation vulnerability in African men with depressive symptoms. The African men with signs of depression displayed higher plasminogen activator inhibitor (PAI-1) levels and marginally elevated D-dimer levels. It was concluded that hypercoagulation may partially be the mediating factor between depressive symptoms and cardiovascular risk in African men; a situation that may be exacerbated by hyperkinetic blood pressure. In conclusion, through the assessement of cardiometabolic function and neuroendocrine responses, it seems that Africans withdepressive symptoms are at great risk for cardiovascular related morbidity and mortality, this was particulary evident in the African men (Manuscript 1 and 3). Additionally, it appears that blunted neuroendocrine responses and hypercoagulation could be seen as possible cardiovascular risk markers in Africans with depressive symptoms. / PhD (Physiology), North-West University, Potchefstroom Campus, 2014

Motivation - Depression is a mental disorder that has been associated with cardiovascular morbidity and mortality in the Western world. Cardiometablic mechanisms have been implicated as possible intermediating factors in the relationship between depressive symptoms and cardiovascular disease; however this has not yet been determined in black Africans (hereafter referred to as Africans). Aim - The overarching aim of this study was to investigate the relationship between depressive symptoms and cardiometabolic risk. We therefore aimed to assess cardiometabolic function, neuroendocrine responses, inflammatory and haemostatic markers in Africans with depressive symptoms compared to those without symptoms of depression. Methodology - Manuscripts presented in Chapter 2, 3 and 4 utilised data from the cross-sectional, target population multi-disciplinary “Sympathetic activity and Ambulatory Blood Pressure in Africans” (SABPA) study. The participants comprised of 200 African teachers from the Dr Kenneth Kaunda District in North-West province, South Africa. As cardiovascular disease is compromised by a positive HIV status, 19 participants were excluded from further statistical analysis. Stratification was based on the Patient Health Questionnaire 9-item (PHQ-9), which has been validated in a sub-Saharan African setting. PHQ-9 scores > 10 were used to classify participants as having signs of depressive symptoms. Subjects were further stratified by gender (Manuscript 1 and 3) and cortisol responses (Manuscript 2). Cardiometabolic health measures included 24-hour blood pressure, metabolic syndrome markers, neuroendocrine markers [cortisol and 3-methoxy-4-hydroxy-phenylglycol (MHPG)], left ventricular hypertrophy (LVH),inflammatory and haemostatic markers (fibrinogen, C-reactive protein, plasminogen activator inhibitor-1 and D-dimer). Resting 12-lead ECG Cornell Product-Left ventricular hypertrophy (CP-LVH) was measured as a marker of target end-organ damage and cardiovascular dysfunction (Manuscript 1 and 2). Means and prevalence were computed through t-test and Chi-square analysis respectively. Significant differences of mean cardiometabolic measures between depressive symptom status groups were also determined by analysis of covariance (adjusted for traditional cardiovascular risk factors and additional factors as specific per manuscript). Multivariate analysis was used to demonstrate associations between left ventricular hypertrophy (LVH) and cardiometabolic markers in Africans with depressive symptoms (Manuscript 1 and 2) and a logistic regression analysis were performed to examine the association between depressive symptoms and inflammatory/haemostatic factors (Manuscript 3). All subjects who participated gave informed consent, the study was approved by the Ethics Committee of North-West University (NWU-0003607S6), in accordance with the principles outlined by the World Medical Association Declaration of Helsinki of 1975 (revised 2008). Results and conclusions of the individual manuscripts - The aim of the study was to investigate the associations between depressive symptoms and cardiometabolic function including cardiovascular dysfunction. Markers of cardiometabolic function assessed were 24 hour blood pressure measurements, metabolic syndrome markers, neuroendocrine markers [cortisol and 3-methoxy-4-hydroxy-phenylglycol (MHPG)], inflammatory and haemostatic variables (fibrinogen, C-reactive protein, plasminogen activator inhibitor-1 and D-dimer). Manuscript 1, focused on LVH as a marker of cardiovascular dysfunction and metabolic syndrome components as markers of cardiometabolic function. The aim of the study was to assess the associations between LVH and metabolic syndrome (MetS) risk markers in participants with and without depressive symptoms. Results revealed that in African men with depressive symptoms the most significant determinants of LVH were systolic blood pressure (SBP) and the percentage glycosylated haemoglobin (HbA1c). While in African women (with depressive symptoms), this association was determined by low high-density lipoprotein (HDL-cholesterol). The study concluded that in black African men, independent of depressive symptoms, cardiometabolic factors (namely SBP and HbA1c) may be the driving significant factors in the development of cardiovascular diseases. Furthermore, the data showed that depressive symptoms in African women were associated with a measure of target end organ damage, and that this association was driven by a metabolic factor. Manuscript 2, the aim of this manuscript was to examine the relationship between depressive symptoms, neuroendocrine responses [with cortisol and 3-methoxy-phenylglycol (MHPG) as markers] and cardiovascular risk, i.e. LVH. The results revealed that Africans with depressive symptoms demonstrated blunted cortisol and MHPG levels in response to acute mental stress, in comparison to those without symptoms of depression. Additionally, these low cortisol and blunted MHPG responses were associated with LVH in this ethnic group. The conclusion for this manuscript was that, blunted neuroendocrine responses linked depressive symptoms and ECG left ventricular hypertrophy in Africans. When coupled to their hypertensive status, these vasoconstrictive responses (cortisol and MHPG) may underpin the increased long-term depression and vascular disease risk in urban Africans. Manuscript 3, the aim of this manuscript was to investigate the relationship between depressive symptoms and inflammatory/haemostatic markers in a cohort of urban-dwelling black African men and women. Our data demonstrated hypercoagulation vulnerability in African men with depressive symptoms. The African men with signs of depression displayed higher plasminogen activator inhibitor (PAI-1) levels and marginally elevated D-dimer levels. It was concluded that hypercoagulation may partially be the mediating factor between depressive symptoms and cardiovascular risk in African men; a situation that may be exacerbated by hyperkinetic blood pressure. In conclusion, through the assessement of cardiometabolic function and neuroendocrine responses, it seems that Africans withdepressive symptoms are at great risk for cardiovascular related morbidity and mortality, this was particulary evident in the African men (Manuscript 1 and 3). Additionally, it appears that blunted neuroendocrine responses and hypercoagulation could be seen as possible cardiovascular risk markers in Africans with depressive symptoms. / PhD (Physiology), North-West University, Potchefstroom Campus, 2014

The subject of spirituality is growing in popularity within the field of mental health. A major aspect of our human experience includes striving for meaning, hopefulness and purpose - this process can be understood as a spiritual experience. Another aspect of our shared human experience includes psychological distress and alienation. This is understood in most contemporary mental health literature as mental disorders. In our contemporary era mental health has addressed the latter. Spirituality, as an integral component of human experience, involves tapping into the innate need for integration while paving the way forward towards a transformative experience. The present research explores important interpretive issues related to spirituality and mental health from within a historical perspective. The present research suggests that holistic trends in mental health cohere with contemporary, phenomenologically rooted trends in spirituality. / Christian Spirituality / M.Th. (Christian Spirituality)

Résumé : Introduction : La ventilation nasale, de plus en plus utilisée chez le nourrisson, peut insuffler de l’air dans l’estomac et causer des reflux gastro-œsophagiens (RGO). Parmi les modes de ventilation nasale, l’aide inspiratoire (AIn) devrait entrainer un plus grand nombre de RGO que le neuro-asservissement de la ventilation assistée (NAVAn), où l’insufflation d’air est plus «physiologique». L’objectif principal de l’étude est de comparer le nombre de RGO en NAVAn et en AIn dans notre modèle ovin d’étude du RGO néonatal et de ventilation nasale. Méthodes : Une polysomnographie avec pH-impédancemétrie œsophagienne de 6 h a été effectuée chez 10 agneaux nouveau-nés. L’enregistrement a été répété trois jours consécutifs (une condition par jour) en respiration spontanée, AIn (15/4 cmH[indice inférieur 2]O) et NAVAn (15/4 cmH[indice inférieur 2]O) dans un ordre randomisé. Résultats : Comparé à la respiration spontanée [13 (23)], le nombre de RGO en 6 h a diminué fortement et de façon similaire en AIn [1 (3)] et en NAVAn [2 (2)] (p < 0,05), même pour des RGO faiblement acides et proximaux. De plus, le nombre d’insufflations d’air n’était pas différent entre l’AIn et la NAVAn. Conclusion : L’AIn et la NAVAn inhibent de façon équivalente les RGO chez l’agneau, incluant les RGO faiblement acides et proximaux, si la pression inspiratoire n’est pas trop élevée et malgré le fait que de l’air soit insufflé dans l’œsophage. Ce résultat est identique à celui obtenu avec l’application d’une pression positive continue nasale (6 cmH[indice inférieur 2]O). Il est possible que la pression positive appliquée lors de la ventilation diminue les relaxations transitoires du sphincter inférieur de l’œsophage, mais des études en manométrie œsophagienne sont nécessaires pour comprendre les mécanismes en jeu. // Abstract : Introduction: Nasal ventilation, increasingly used in infants, can blow air in the stomach and cause gastroesophageal reflux (GER). Among the nasal ventilation modes, pressure support ventilation (nPSV) should lead to a greater number of GER than neurally-adjusted ventilatory assist (nNAVA), where the air delivery is more "physiological". The main objective of the study is to compare the number of GER in nNAVA and nPSV in our unique sheep model of neonatal GER and nasal ventilation. Methods: A 6h polysomnographic recording with esophageal pH-impedance was performed in 10 newborn lambs. The recording was repeated for three consecutive days (one condition per day) for spontaneous breathing, nPSV (15/4 cmH[subscript 2]O) and nNAVA (15/4 cm H[subscript 2]O) in a randomized order. Results: Compared with spontaneous breathing [13 (23)], the number of GER in 6h strongly and similarly decreased in nPSV [1 (3)] and nNAVA [2 (2)] (p < 0.05), even proximal and weakly acidic GER. In addition, the number of air insufflations was not different between nPSV and nNAVA. Conclusion: nPSV and nNAVA both inhibit GER in lambs, including weakly acidic and proximal GER, if the inspiratory pressure is not too high and despite the fact that air is blown into the esophagus. This result is identical to the one obtained with the application of a nasal continuous positive airway pressure (6 cmH[subscript 2]O). It is posssible that the applied positive pressure decreases transient relaxations of the lower esophageal sphincter, but esophageal manometry studies are needed to understand the mechanisms involved.