Dissertations / Theses on the topic 'Respiratory signal processing'

Le photopléthysmogramme (PPG) est un signal optique acquis à partir de l’oxymètre de pouls, dont l’usage principal consiste à mesurer la saturation en oxygène. Avec le développement des technologies portables, il est devenu la technique de base pour la surveillance de l’activité cardio-respiratoire des patients et la détection des anomalies. En dépit de sa simplicité d'utilisation, le déploiement de cette technique reste encore limité pour deux principales raisons : 1. L’extrême sensibilité du signal aux distorsions. 2. La non-reproductibilité entre les sujets et pour les mêmes sujets, en raison de l'âge et des conditions de santé. L’objectif de cette thèse est le développement de méthodes robustes et universelles afin d’avoir une estimation précise de la fréquence respiratoire (FR) indépendamment de la variabilité intra et interindividuelle du PPG. Plusieurs contributions originales en traitement statistiques du signal PPG sont proposées. En premier lieu, une méthode adaptative de détection des artefacts basée sur la comparaison de modèle a été développée. Des tests par la technique Random Distortion Testing sont introduits pour détecter les pulses de PPG avec des artefacts. En deuxième lieu, une analyse de plusieurs méthodes spectrales d’estimation de la FR est proposée. Afin de mettre en évidence la robustesse des méthodes proposées face à la variabilité du signal, plusieurs tests ont été effectués sur deux bases de données avec de différentes tranches d'âge et des différents modes respiratoires. En troisième lieu, un indice de qualité respiratoire spectral (SRQI) est conçu dans le but de communiquer au clinicien que les valeurs fiables de la FR avec un certain degré de confiance
One promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases

This thesis investigates the development and use of software to measure respiratory frequency on cows using optronics and computer vision. It examines mainly two different strategies of image and signal processing and their performances for different input qualities. The effect of heat stress on dairy cows and the high transmission risk of pneumonia for calves make the investigation done during this thesis highly relevant since they both have the same symptom; increased respiratory frequency. The data set used in this thesis was of recorded dairy cows in different environments and from varying angles. Recordings, where the authors could determine a true breathing frequency by monitoring body movements, were accepted to the data set and used to test and develop the algorithms. One method developed in this thesis estimated the breathing rate in the frequency domain by Fast Fourier Transform and was named "N-point Fast Fourier Transform." The other method was called "Breathing Movement Zero-Crossing Counting." It estimated a signal in the time domain, whose fundamental frequency was determined by a zero-crossing algorithm as the breathing frequency. The result showed that both the developed algorithm successfully estimated a breathing frequency with a reasonable error margin for most of the data set. The zero-crossing algorithm showed the most consistent result with an error margin lower than 0.92 breaths per minute (BPM) for twelve of thirteen recordings. However, it is limited to recordings where the camera is placed above the cow. The N-point FFT algorithm estimated the breathing frequency with error margins between 0.44 and 5.20 BPM for the same recordings as the zero-crossing algorithm. This method is not limited to a specific camera angle but requires the cow to be relatively stationary to get accurate results. Therefore, it could be evaluated with the remaining three recordings of the data set. The error margins for these recordings were measured between 1.92 and 10.88 BPM. Both methods had execution time acceptable for implementation in real-time. It was, however, too incomplete a data set to determine any performance with recordings from different optronic devices.

Sleep is an important part of everyday life. It directly affects daytime cognition and general performance. In children, sleep is a crucial requirement for growth and learning and lack of sleep may manifest itself as a long lasting developmental deficit. Sleep disorders which disrupt the normal continuity of sleep therefore benefit from early identification and treatment. A common cause of sleep disruption is sleep disordered breathing which can be associated with frequent arousals from sleep. Many relevant areas of sleep research continue to generate new and interesting findings utilising biosignals such as EEGs. Respiratory cycle related EEG change (RCREC) is a good example of this. The method for quantification of RCREC relies on the appropriate application of signal processing and the signals involved in the procedure are polysomnographic. Furthermore, RCREC is thought to reflect morbid micro-arousals in sleep and is hence also of clinical importance. Given that the field of RCREC research is a recently established one, there is much room for constructive investigation. The current state of RCREC research is therefore expanded in this thesis. The method for calculation of respiratory cycle related EEG change (RCREC) is replicated and expanded in this project. Shortcomings of the method have been identified and accounted for where appropriate. In particular, the sensitivity of RCREC to airflow signal segmentation is addressed and alternative segmentation approaches are suggested. The general influence of airflow segmentation on RCREC is investigated and a mathematical explanation for RCREC sensitivity is given. Additionally, the ability of RCREC related parameters to predict daytime cognitive functions is assessed. Results suggest that RCREC parameters are capable of predicting quality of episodic memory, power (speed) of attention and internal processing speed.

La mesure par voie de surface du signal electromyographique (emg) des muscles respiratoires est perturbee par l'artefact cardiaque, le bruit de l'electronique de conditionnement et le bruit des electrodes de mesure. Nous proposons dans le cas de l'artefact cardiaque deux methodes de filtrage; l'une de ces deux methodes est basee sur un filtrage adaptatif applicable en temps reel. Pour le bruit de l'electronique et des electrodes, nous avons mis au point une solution utilisant un reseau d'electrodes permettant de rejecter ce bruit a l'aide d'un traitement multidimensionnel. Une carte-dsp de traitement et d'acquisition multivoies permettant l'implementation temps reel des procedures de traitement et de calcul des parametres physiologiques a ete developpee. Le calcul de ces parametres est synchronise sur le signal de debit ventilatoire ce qui nous permet de calculer les parametres inspiratoires et expiratoires separement

2013/2014
High Frequency Percussive Ventilation (HFPV) is a non-conventional ventilatory modality which has proven highly effective in patients with severe gas exchange impairment. However, at the present time, HFPV ventilator provides only airway pressure measurement. The airway pressure measurements and gas exchange analysis are currently the only parameters that guide the physician during the HFPV ventilator setup and treatment monitoring. The evaluation of respiratory system resistance and compliance parameters in patients undergoing mechanical ventilation is used for lung dysfunctions detection, ventilation setup and treatment effect evaluation. Furthermore, the pressure measured by ventilator represents the sum of the endotracheal tube pressure drop and the tracheal pressure. From the clinical point of view, it is very important to take into account the real amount of pressure dissipated by endotracheal tube to avoid lung injury. HFPV is pressure controlled logic ventilation, thus hypoventilation and hyperventilation cases are possible because of tidal volume variations in function of pulmonary and endotracheal tube impedance. This thesis offers a new approach for HFPV ventilator setup in accordance with protective ventilatory strategy and optimization of alveolar recruitment using estimation of the respiratory mechanics parameters and endotracheal pressure drop. Respiratory system resistance and compliance parameters were estimated, firstly in vitro and successively in patients undergoing HFPV, applying least squares regression on Dorkin high frequency model starting from measured respiratory signals. The Blasius model was identified as the most adequate to estimate pressure drop across the endotracheal tube during HFPV. Beside measurement device was developed in order to measure respiratory parameters in patients undergoing HFPV. The possibility to tailor HFPV ventilator setup, using respiratory signals measurement and estimation of respiratory system resistance, compliance and endotracheal tube pressure drop, provided by this thesis, opens a new prospective to this particular ventilatory strategy, improving its beneficial effects and minimizing ventilator-induced lung damage.
XXVII Ciclo
1981

Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT IGRT requires precise respiratory signal extraction to avoid burry edges, inaccurate tumor shape, and motion-induced artifacts on the reconstructed CBCT image. 4D MRIs used in MRI-guided radiation therapy typically have low resolution as a tradeoff with field of view, image acquisition time, and image quality. To predict the tumor motion and guarantee the superior quality of the imaging techniques, the dissertation is divided into three parts. The first part describes a new prediction method for respiration-related tumor movements, called Intra- and Inter-fractional variation prediction using Fuzzy Deep Learning (IIFDL). IIFDL clusters the respiratory movements based on breathing similarities, and estimates patients' breathing motion using the proposed predictor, called fuzzy deep learning. The second part of the dissertation includes a novel marker-less binning method for 4D CBCT projections, called Image Registration-based Projection Binning (IRPB), which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing periodicity of tissue feature point trajectories. The third part the dissertation explains a novel Super-Resolution (SR) method for 4D MRI, called Recurrent Deep Learning-based SR (RDLS), comprised of feature extraction, recurrent nonlinear mapping, and reconstruction. RDLS estimates high-resolution MRIs from low-resolution MRIs according to a specified magnification power.

2012/2013
The use of High Frequency Percussive Ventilation (HFPV) is still debated although this type of non-conventional ventilation has proven effective and safe in patients with acute respiratory failure. In the clinical practice, HFPV is not an intuitive ventilatory modality and the absence of real-time delivered volume monitoring produces disaffection among the physicians. Avoiding the "volutrauma" is the cornerstone of the "protective ventilation strategy", which assumes a constant monitoring of inspiratory volume delivered to the patient. Currently the system capable of delivering HFPV is the VDR-4® (Volumetric Diffusive Respirator), which provides only analog airway pressure waveform and digital output of peak and the mean airway pressure. The latter is involved in the determination of oxygenation and hemodynamics, irrespective of the mode of ventilation. At the present time, the mean airway pressure, together with gas exchange analysis, are the only parameters that indirectly guide the physician in assessing the clinical effectiveness of HFPV. Till now, flow, volume and pressure curves generated by HFPV have never been studied in relation to the specific patients respiratory mechanics. The real-time examination of these parameters could allow the physicians to analyze and understand elements of respiratory system mechanics as compliance (Crs), resistance (Rrs), inertance (Irs) and of patient-ventilator interaction. The mechanical effects are complex and result from interactions between ventilator settings and patient’s respiratory system impedance. The aim of this doctoral thesis was to acquire and study volume and respiratory parameters during HFPV in order to explain this complex patients-machine interaction and transfer the results in clinical practice.
XXVI Ciclo
1959

L'objectif de cette thèse est d'étudier temporellement des phénomènes évolutifs à l'aide de la tomographie d'émission monophotonique (TEMP). La première partie de cette thèse traite le problème du mouvement respiratoire dans les images TEMP de souris. Nous présentons ici une méthode permettant de détecter ce mouvement respiratoire dans les images TEMP 4D, d'extraire un signal respiratoire intrinsèque, et de déterminer la phase du cycle respiratoire sans mouvement la plus large possible. Les données enregistrées durant ces phases sans mouvement sont alors utilisées pour reconstruire une seule image TEMP 3D sans artefact de mouvement par acquisition. Les images ainsi reconstruites présentent un bon compromis en terme de statistiques et de précision des mesures par rapport aux images TEMP 3D de base et TEMP 4D. Dans la deuxième partie, nous étudions la cinétique d'incorporation de l'iode dans l'estomac de souris à partir d'images TEMP 4D. Afin de comprendre le rôle biologique de cette accumulation dans l'estomac, nous avons modélisé le phénomène par une approche d'analyse compartimentale avec un modèle simplifiée à deux compartiments (paroi et cavité stomacale) et une entrée (sang). Les courbes temps-activité (TAC) de chaque compartiment sont déduites des observations et une première estimation des paramètres a été obtenue.

Le rythme respiratoire est une information importante dans le contexte médical puisqu'elle permet de prédire un certain nombre de complications potentiellement mortelles.Malgré cela, elle est souvent négligée par le personnel médical faute de temps ou de bien comprendre les enjeux associés.Dans ce contexte, les méthodes de mesure automatisées permettent d'améliorer le statu quo en fournissant en continu une mesure du rythme respiratoire.La plupart des méthodes actuelles comme la ceinture respiratoire ou l'ECG nécessitent un contact avec la personne pour pouvoir mesurer efficacement le rythme respiratoire.Malheureusement, cela introduit un certain nombre de problèmes qui peuvent empêcher la mesure dans certains cas ou la rendre contraignante lors d'une mesure en continu et au quotidien, là où il serait souhaitable que la mesure soit la plus discrète possible.Afin de pallier à ces problèmes, plusieurs méthodes de mesure du rythme respiratoire sans contact sont actuellement en développement.Parmi celles-ci, la photopléthysmographie sans contact utilise la variation de la couleur de la peau en fonction du volume sanguin présent dans les capillaires afin de trouver un signal cardiaque et respiratoire.Dans la thèse présentée, nous nous attachons à améliorer la qualité de la mesure du rythme respiratoire à l'aide de la photopléthysmographie sans contact en développant des méthodes dont le but est de combiner efficacement les signaux couleur extraits à partir d'une vidéo de manière à obtenir un seul signal maximisant l'information respiratoire.Dans un deuxième temps, une chaîne de traitement est mise en place de façon à utiliser ces méthodes de combinaison pour déterminer le rythme respiratoire en utilisant toutes les informations pouvant être extraites du signal photopléthysmographique
Respiratory rhythm is important information in medical context.Its assessment allows to predict some medical complications that could lead to death.However, it is often neglected by the medical staff due to a bad comprehension of its importance, or a lack of time.Automated measurement methods allow to improve this by continuously giving respiratory rate.Most of these methods needs a contact with the patient to efficiently measure the breathing rate.Unfortunately it leads to some issues which could forbid measurement or make it unconfortable for continuous monitoring.The continuous, every-day monitoring especially needs to be as discrete as possible to be forgotten by the patient.To deal with these drawbacks, several non contact respiratory rate assessment methods are currently developped.In these methods, the remote photoplethysmography uses the color variation of the skin due to blood volume in capillaries to obtain a cardiac and respiratory correlated signal.In this thesis, we focused on improving the respiratory rate measurement using remote photoplethysmography.To do this, we developped some methods whose goals are to efficiently combine color signals that were extracted from the video to obtain a single signal that maximizes the breathing information.In a second part, we developped a processing pipeline to assess the breathing rate from all informations that could be extracted from remote photoplethysmography signals.The processing pipeline was tested with state of the art combination methods and the methods that were developped during the thesis

La tomographie par émission de positons (TEP) est une modalité d'imagerie fonctionnelle incontournable pour le diagnostic et le suivi thérapeutique en oncologie. De nouvelles applications telles que la radiothérapie guidée par l'imagerie fonctionnelle sont en cours d'investigation. Les images TEP souffrent toutefois d'une faible résolution spatiale, encore dégradée par les effets du mouvement respiratoire du patient dans le thorax et l'abdomen. La grande majorité des solutions proposées pour la correction de ce mouvement respiratoire reposent sur l'enregistrement du signal respiratoire pendant l'acquisition TEP et de la synchronisation de l'acquisition avec ce signal respiratoire. Les données peuvent ainsi être séparées selon la partie du cycle respiratoire pendant laquelle elles ont été acquises. Les données correspondant à une même position peuvent ensuite être sommées et reconstruites. Les images résultantes sont cependant de qualité réduite, car elles ne contiennent qu'une portion de l'information. Il est donc nécessaire de les combiner. Les solutions disponibles actuellement proposent de recaler et sommer les données synchronisées, avant, pendant, ou après leur reconstruction, ce qui produit une image sans mouvement de qualité proche de celle qui aurait pu être obtenue en l'absence de mouvement respiratoire. La super-résolution vise à améliorer la résolution d'une image appartenant à une séquence d'images représentant différentes vues de la même scène. Elle exploite le mouvement présent dans cette séquence afin d'obtenir une image d'une résolution supérieure à celle permise par le système d'imagerie et ne contenant pas de recouvrement de spectre. Le but de cette thèse est d'appliquer une telle technique pour compenser le mouvement respiratoire. Nous avons d'abord appliqué un algorithme de super-résolution déjà existant à une séquence d'images TEP synchronisées avec la respiration, ce qui représente une application inédite. Cette technique permet de corriger efficacement les effets du mouvement respiratoire. Les méthodes de correction du mouvement respiratoire sont souvent plus performantes lorsqu'elles sont incorporées à la reconstruction plutôt qu'appliquées aux images reconstruites. C'est pourquoi nous avons ensuite développé de nouveaux algorithmes de reconstruction TEP incorporant la super-résolution. Les images ainsi reconstruites sont de meilleure qualité que celles corrigées par super-résolution après reconstruction. Enfin, nous avons montré que la correction du mouvement respiratoire par super-résolution permet une précision accrue dans la planification du traitement par radiothérapie.

CAPES
A insuficiência respiratória gerada pela lesão medular, em pessoas com tetraplegia e paraplegia torácica alta, tem sido uma das principais causas de morte desses indivíduos. A paralisação, total ou parcial, dos músculos abdominais e do diafragma dificulta a produção de tosse e diminui o volume corrente da ventilação. Este problema pode ser amenizado por meio do tratamento com estimulação elétrica funcional transcutânea (EEFT), na musculatura diafragmática e abdominal, sincronizada com a respiração espontânea. Poucos estudos têm sido direcionados a esta área e foi constatado que é de grande interesse científico que seja desenvolvido um sistema capaz de automaticamente sincronizar a estimulação elétrica com os eventos de inspiração (estimulação diafragmática) e expiração (estimulação abdominal). Por isso, nesta dissertação, desenvolveu-se um sistema de aquisição de sinal respiratório e detecção dos eventos de inspiração e expiração para sincronismo da EEFT durante a respiração tranquila. O sistema emprega uma cinta elástica acoplada a uma célula de carga baseada em strain gauges para a aquisição do sinal respiratório. Um algoritmo, baseado em análise estatística do sinal, foi desenvolvido para a detecção das fases de inspiração e expiração.Testes foram realizados em oito voluntários hígidos. A cinta foi posicionada na região da última costela, e sinais foram adquiridos com o auxílio de um osciloscópio digital. Um fisioterapeuta ajudou na análise dos sinais. Foi realizada a contagem de inspirações e expirações detectadas corretamente. O resultado dos testes alcançou a taxa de 82% de acerto na detecção dos eventos inspiratórios, e 96% para os eventos expiratórios. Os resultados indicam que o sistema desenvolvido é eficiente para a aquisição de sinais respiratórios e o algoritmo criado pode propiciar a sincronização da EEFT, com o paciente tratado em posição quase estática.
The respiratory failure, caused by spinal cord injury in people with high thoracic paraplegia and tetraplegia, has been the major cause of death for those individuals. The total or partial paralysis of the abdominal muscles and the diaphragm hinders the production of cough and decreases tidal volume. This problem can be alleviated by treatment with transcutaneous functional electrical stimulation (TFES), on diaphragm and abdominal muscles synchronized with the spontaneous respiration. Few studies have been conducted on this matter, and it was found that is of great scientific interest the development of a system capable of automatically triggering the electrical stimulation with inspiration (diaphragmatic stimulation) and expiration events (abdominal stimulation). Therefore, in this work, a respiratory signal acquisition system was developed for the detection of inspiration and expiration events for triggering the electrical stimulation during quiet breathing. The system employs an elastic belt attached to a load cell based on strain gauges for acquiring the respiratory signal. An algorithm based on signal statistical analysis was developed for the detection of inspiration and expiration events. Tests were carried out in eight healthy volunteers. The belt was positioned at the last rib region, and signals were obtained with the aid of a digital oscilloscope. A physical therapist helped in the analysis of the signals by counting the inspiratory and expiratory events. The results reached the accuracy of 82% in the detection of inspiratory events, and 96% for expiratory events. The results indicate that the developed system is effective for the acquisition of respiratory signals and the created algorithm can provide synchronization of TFES with the patient in quasi-static situation during treatment.

Pacientes portadores de doença pulmonar obstrutiva crônica (DPOC) podem apresentar assincronia toracoabdominal (ATA). Existem diversos métodos de estimativa da ATA, porém, não há um consenso sobre qual é o mais adequado. O objetivo deste estudo foi comparar dois métodos de estimativa da assincronia toracoabdominal e avaliar a ineficiência ventilatória em pacientes DPOC no repouso e durante o exercício. Foram avaliados 22 pacientes com DPOC (VEF1 40,2±10,5% predito) e 13 indivíduos controle (GC) pareados por idade, gênero e índice de massa corpórea. A cinemática toracoabdominal foi avaliada utilizando pletismografia optoeletrônica no repouso e durante o exercício leve e moderado (70% da carga máxima) no ciclo ergômetro. A ATA foi calculada entre a caixa torácica superior (CTS) e inferior (CTI) e o abdome (ABD) utilizando os métodos de ângulo de fase (AF) e relação de fase (RF). A ineficiência ventilatória foi calculada em cada compartimento como a diferença entre o volume máximo (VM) e o volume calculado (VC) de acordo com o ciclo respiratório (determinado pela soma de volume dos três compartimentos) dividida pelo volume máximo (VM-VC)/VM. Os pacientes com DPOC foram classificados como assíncronos (grupo AT) ou não assíncronos (grupo NA) utilizando como referência os valores do GC. Foi utilizado o teste qui-quadrado ou de Fisher para avaliar a discriminação de pacientes entre os métodos e o ANOVA de dois fatores para comparações entre os grupos. O nível de significância foi ajustado para 5%. O método AF determinou maior número de pacientes com ATA quando comparado com RF no repouso (respectivamente, 15 vs. 7) e no exercício leve (11 vs. 3) e moderado (14 vs. 8). Os valores de assincronia no grupo AT entre CTS-CTI e CTI-ABD foram maiores no repouso (AF: 35,7±45,4° e -42,2±42,5° e RF: 61,8±29,1° e -66,9±27,4°, respectivamente) e no exercício leve (AF: 53,3±35,6° e -55,8±40,4°; RF: 106,1±40,3° e - 124,8±17,2°) e moderado (AF: 61,6±55,1° e -75,9±44,8°; RF: 85,9±23,6° e -81,8±42,2°) quando comparados com os grupos NA (p < 0,05) e GC (p < 0,05). Na análise entre CTSABD não houve diferença entre os grupos. Observou-se que o grupo AT apresentou menor contribuição e maior ineficiência ventilatória da CTI em todos os momentos de avaliação e, durante o exercício moderado, menor volume corrente quando comparado com os grupos NA e GC. Os nossos resultados sugerem que o ângulo de fase apresenta maior detecção de ATA nos pacientes com DPOC. A presença de assincronia parece ocorrer principalmente na caixa torácica inferior e associada com menor contribuição e maior ineficiência ventilatória deste compartimento
Chronic obstructive pulmonary disease (COPD) patients can present thoracoabdominal asynchrony (TAA). There are several TAA estimation techniques, however, there is no consensus about which is the most appropriate. The aim of this study was to compare two thoracoabdominal asynchrony quantification techniques and to assess chest wall ventilatory inefficiency in COPD patients at rest and during exercise. We evaluated 22 COPD patients (FEV1 40,2±10,5% predicted) and 13 healthy controls (CG) matched by age, gender and body mass index. Thoracoabdominal kinematics was assessed via optoelectronic plethysmography at rest and during mild and moderate exercise (70 % maximum workload) in a cycle ergometer. TAA was calculated among upper (URC) and lower ribcage (LRC) and abdomen (ABD) by using the phase angle (PA) and phase relation (PR) approaches. Ventilatory Inefficiency was estimated in each compartment as the difference between the maximal volume (VM) and the volume (VC) calculated according to respiratory timing (sum of volume in the 3 compartments) divided by the maximal volume (VM-VC)/VM. COPD patients were classified as asynchronous (AT group) or not (NA group) by using as reference the values on the controls. Chi-square or Fisher\'s exact test was used for assessing the patients differentiation between the two TAA quantification approaches and two-way ANOVA was used to compare respiratory parameters among groups (CG, AT and NA). Statistical significance was set at 5% level. PA approach determined more patients as asynchronous when compared to RF at rest (respectively, 15 vs. 7) and during mild (11 vs. 3) and moderate (14 vs. 8) exercise. Asynchrony values in AT group among URC-LRC and LRC-ABD were greater at rest (respectively, 35.7±45.4° and -42.2±42.5° with PA and 61.8±29.1° and -66.9±27.4° with PR) and during mild (PA: 53.3±35.6° and -55.8±40.4°; PR: 106.1±40.3° and -124.8±17.2°) and moderate exercise (PA: 61.6±55.1° and - 75.9±44.8°; PR: 85.9±23.6° and -81.8±42.2°) when compared to NA (p < 0.05) and CG (p < 0.05). Analysis among URC-ABD presented no difference between groups. It was observed that AT group presented a smaller LRC contribution and greater ventilatory inefficiency during all assessing moments and, during moderate exercise, had a lower tidal volume when compared to NA and CG. Our results suggest that phase angle approach presents larger TAA detection in COPD patients. This asynchrony seems to occur mainly in the lower ribcage and be associated with decreased contribution and increased ventilatory inefficiency of this compartment

Le syndrome d'apnée du sommeil (SAS) est une maladie multifactorielle caractérisée par des épisodes récurrents de pauses respiratoires ou des réductions significatives de l'amplitude respiratoire pendant le sommeil. Ces épisodes peuvent provoquer des réactions cardiorespiratoires aiguës; délétères à long terme. Plusieurs thérapies ont été proposées, étant la pression positive continue des voies respiratoires (CPAP) le traitement de référence. Malgré ces excellents résultats chez les patients symptomatiques, le taux de refus initial est de 15% et une adhésion à long terme est difficile à atteindre. Par conséquent, le développement de méthodes de traitement non invasives, avec une meilleure acceptabilité, reste d’une importance majeure. Dans ce contexte, l’hypothèse qui sous-tend ce travail est qu’une stimulation kinesthésique contrôlée, délivrée au cours de la phase précoce de l’apnée, peut réduire la durée des événements respiratoires et, par la suite, limiter les désaturations d’oxygène associées, par une activation contrôlée du réflexe de sursaut. La première partie de ce manuscrit est consacrée à la description d'un nouveau système (PASITHEA) de surveillance en temps réel et de neuromodulation thérapeutique, qui fonctionne comme un dispositif polyvalent de diagnostic et de traitement de SAS par stimulation kinesthésique. Les principales contributions de cette thèse se concentrent sur les aspects du traitement du signal et du contrôle de ce système, ainsi que sur l'électronique associée. Une autre contribution est liée à l'évaluation de ces méthodes et dispositifs par des protocoles cliniques spécifiques. Dans une deuxième partie, nous proposons une première méthode de contrôle On/Off optimale pour délivrer la stimulation, en utilisant comme variable de contrôle la sortie d'un détecteur d'événements respiratoires en temps réel. Lors de la détection d'un événement, une stratégie de stimulation unique avec amplitude de stimulation constante est appliquée, cette dernière a été mise en œuvre dans le cadre d'un premier protocole clinique dédié à l'évaluation de la réponse du patient au traitement. Les résultats ont montré que 75% des patients répondaient correctement au traitement en termes de durées des épisodes respiratoires. De plus, des diminutions significatives de la variabilité du SaO2 ont également été constatées lors de la mise en œuvre d'une nouvelle méthode d'analyse aiguë. Puisque nous avons supposé qu'une sélection inappropriée des patients pourrait expliquer l'absence de réponse observée chez 25% des patients. Nous avons proposé une méthode pour différencier les patients qui pourraient bénéficier de cette thérapie, basée sur l'estimation d'indices de variabilité cardiaque. Les résultats de ces analyses ont montré que l'efficacité de cette thérapie semble corrélée à un système nerveux autonome fonctionnel. Enfin, une méthode améliorée de contrôle en boucle fermée, intégrant des correcteurs proportionnels-dérivés (PD) couplés et simultanés a été proposée afin de modifier de façon adaptative l’amplitude de stimulation kinesthésique délivrée au patient par le système thérapeutique, en utilisant comme variables de contrôle des signaux physiologiques enregistrés en temps réel. Un deuxième protocole clinique visant à valider l'algorithme de contrôle de la stimulation kinesthésique adaptative spécifique au patient a été initié. Plusieurs améliorations ont été effectuées à la première version du système afin de permettre l'intégration du contrôleur proposé. Les résultats préliminaires de cette étude ont validé le fonctionnement de notre contrôleur et ont montré que notre système était capable de fournir une stimulation kinesthésique adaptative en fonction des réponses propres au patient. Une autre phase de cette étude, mettant en œuvre le contrôleur avec un ensemble des paramètres de contrôle présélectionnés, est actuellement en cours
Sleep apnea syndrome (SAS) is a multifactorial disease characterized by recurrent episodes of breathing pauses or significant reductions in respiratory amplitude during sleep. These episodes may provoke acute cardiorespiratory responses along with alterations of the sleep structure, which may be deleterious in the long term. Several therapies have been proposed for the treatment of SAS, being continuous positive airway pressure the gold standard treatment. Despite its excellent results in symptomatic patients, there is a 15% initial refusal rate and long term adherence is difficult to achieve in minimally symptomatic patients. Therefore, the development of non-invasive SAS treatment methods, with improved acceptability, is of major importance. The objective of this PhD thesis is to propose new signal processing and control methods of non-invasive neuromodulation for the treatment of SAS. The hypothesis underlying this work is that bursts of kinesthetic stimulation delivered during the early phase of apneas or hypopneas may elicit a controlled startle response that can activate sub-cortical centers controlling upper airways muscles and the autonomic nervous system, stopping respiratory events without generating a cortical arousal. In this context, the first part of this manuscript is dedicated to the description of a novel real-time monitoring and therapeutic neuromodulation system, which functions as a multi-purpose device for SAS diagnosis and treatment through kinesthetic stimulation. This system has been developed in the framework of an ANR TecSan project led by our laboratory, with the participation of Sorin CRM SAS. The main contributions in this thesis are focused on the signal processing and control aspects of this system, as well as the electronics associated. Another contribution is related to the evaluation of these methods and devices through specific clinical protocols. In a second part, we propose a first optimal On/Off control method for delivering kinesthetic stimulation, using as control variable the output of a real-time respiratory event detector. A unique stimulation strategy where a constant stimulation amplitude is applied upon event detention was implemented in a first clinical protocol, dedicated to assessing the patient response to therapy. Results showed that 75% of the patients responded correctly to therapy, showing statistically significant reductions in respiratory event durations. Also, significant decreases in the SaO2 variability were also found when implementing a novel acute analysis method. Since we hypothesized that inappropriate patient selection could explain the observed lack of response in 25% of patients, we proposed a method to differentiate patients who could benefit from this therapy based on the estimation of complexity-based indexes of heart rate variability. Results of these analyses showed that the effectiveness of this therapy seems correlated to a functional autonomic nervous system. Finally, an improved closed-loop control method integrating concurrent, coupled proportional-derivative (PD) controllers in order to adaptively change the kinesthetic stimulation was proposed. It uses as control variables three physiological signals recorded in real-time: Nasal pressure, oxygen saturation and the electrocardiogram signal. A second clinical protocol with the main objective of validating the control algorithm for patient-specific adaptive kinesthetic stimulation was launched. Several improvements to the first version of the system were developed to allow the integration of the proposed controller. Preliminary results from the first phase of this study validated the proposed controller operation and showed that the controller was able to provide adaptive kinesthetic stimulation in function of the patient-specific responses. A second phase of this study implementing the proposed controller and the set of the selected control parameters from the first phase is currently ongoing

Кваліфікаційна робота виконана на кафедрі біотехнічних систем Тернопільського національного технічного університету імені Івана Пулюя
Наведено результати розробки комп'ютерного засобу тестування методів обробки сигналів дихання як шуму повторного та амплітудо-модульованого методами імітаційного моделювання та програмного засобу Matlab. Імітаційна модель сигналу дихання забезпечує за даними медичних показників (морфопараметрів) імітувати сигнали різних станів органів дихання, що є важливим при тестуванні методів та розроблених їх підґрунті програмних засобів обробки сигналів у комп’ютерних аускультаційних системах. Імітаційна модель сигналу дихання реалізована через відповідне програмне забезпечення (комп’ютерний засіб) в Matlab та є адекватним засобом імітації сигналів дихання різни станів з мінімальними відхиленнями від емпіричних сигналів.
The results of development of a computer tool for testing methods of processing respiratory signals as repeated and amplitude-modulated noise by simulation methods and Matlab software are presented. The simulation model of the respiratory signal provides, according to medical indicators (morphoparameters), to simulate the signals of different respiratory conditions, which is important when testing methods and software based on signal processing in computer auscultation systems. The simulation model of the respiratory signal is implemented through the appropriate software (computer tool) in Matlab and is an adequate means of simulating respiratory signals of different states with minimal deviations from the empirical signals.
ВСТУП 8 РОЗДІЛ 1. АНАЛІТИЧНА ЧАСТИНА 10 1.1. Процес утворення сигналів дихання 10 1.2. Сигнал дихання та його показники 12 1.3. Відомі матмоделі сигналів дихання 17 1.3.1. Стаціонарний випадковий процес 17 1.3.2. Стаціонарна випадкова центрована функція та періодична функція 18 1.3.3. Адитивна суміш шумової та періодичної компонент 19 1.3.4. ПКВП 22 1.4. Висновки до розділу 1 23 РОЗДІЛ 2. ОСНОВНА ЧАСТИНА 24 2.1. Реєстрація сигналів дихання 24 2.2. Оцінювання показників сигналу дихання 26 2.3. Матмодель сигналів дихання 31 2.4. Суть тестування методів обробки сигналів дихання 34 2.5. Імітаційна модель сигналів дихання 36 2.6. Узагальнене подання алгоритму імітування сигналів дихання 45 2.7. Блок-схема імітаційного моделювання дихального шуму 46 2.8. Висновки до розділу 2 48 РОЗДІЛ 3. НАУКОВО-ДОСЛІДНА ЧАСТИНА 49 3.1. Блок-схема комп’ютерного засобу тестування методів обробки сигналів дихання 49 3.2. Блок-схема комп’ютерного засобу тестування 50 3.3. Реалізація комп’ютерного засобу тестування 51 3.4. Результати роботи комп’ютерного засобу тестування 55 3.5. Висновки до розділу 3 57 7 РОЗДІЛ 4. ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ 58 4.1 Охорона праці 58 4.2 Безпека в надзвичайних ситуаціях 61 4.3 Висновки до розділу 4 65 ЗАГАЛЬНІ ВИСНОВКИ 66 ПЕРЕЛІК ПОСИЛАНЬ 67 ДОДАТКИ 78 ДОДАТОК А. Програмне забезпечення комп’ютерного засобу тестування методів обробки сигналів дихання 79 ДОДАТОК Б. Копія тези 81 ДОДАТОК В. Копія тези 83

碩士
國立清華大學
通訊工程研究所
101
This paper presents a ultra-wideband (UWB) impulse-radio radar signal processing platform. This platform is integrated with a front-end radar chip for human respiratory feature extraction and signal compression. The conventional radar detection algorithms only extract the respiration rate for medical diagnosis. However, there is more information in the radar-detected respiratory signals which can be useful for medical diagnosis. Thus, this study proposed a modified raised cosine model and an iterative correlation algorithm to extract more respiratory features, such as inspiration and expiration speed, respiration intensity, and respiration holding ratio. Moreover, the extracted features are useful in remote medical monitoring system since they can be seen as compressed respiratory signals. Transmission bandwidth can be saved by transmitting the extracted features instead of lots of sampled data. The proposed algorithm and architecture is designed and implemented on a radar signal processing platform with the ARM processor and FPGA logic array. Human respiratory signals of 0.1 to 1 Hz rate are detected and analyzed along with other information at each period.

碩士
國立清華大學
電機工程學系
102
This paper presents a real time ultra-wideband (UWB) impulse-radio radar signal processing platform with reduced complexity. This platform is integrated with a radar front-end chip for human respiratory feature extraction and signal compression. Conventional radar detection algorithms only extract respiration rate for medical diagnosis. However, there is more useful information in the radar-detected respiratory signals for medical diagnosis. Thus, this study proposed a four segment linear wave model and an iterative correlation search algorithm to extract more respiratory features, such as inspiration and expiration speed, respiration intensity, and respiration holding ratio between inspiration and expiration. Moreover, since the iterative correlation search algorithms involves high computation cost, this study applies an early termination scheme and down sampling to reduce the complexity with negligible performance degradation. The extracted features are also useful in a remote medical monitoring system because they can be regarded as compressed respiratory signals. One-period human respiratory cycle can be expressed by extracted features instead of lots of samples. Transmission bandwidth or storage capacity can be greatly saved by transmitting or storing the extracted features. The proposed algorithm and architecture was designed and implemented on a real time radar signal processing platform with a FPGA chip. Human respiratory signals from 0.1 to 1 Hz rate are detected and analyzed along with other information in each period.

Tracheal respiratory sounds analysis has been investigated as a non-invasive method to estimate respiratory flow and upper airway obstruction. However, the flow-sound relationship is highly variable among subjects which makes it challenging to estimate flow in general applications. Therefore, a robust model for acoustical flow estimation in a large group of individuals did not exist before. On the other hand, a major application of acoustical flow estimation is to detect flow limitations in patients with obstructive sleep apnea (OSA) during sleep. However, previously the flow--sound relationship was only investigated during wakefulness among healthy individuals. Therefore, it was necessary to examine the flow-sound relationship during sleep in OSA patients. This thesis takes the above challenges and offers innovative solutions. First, a modified linear flow-sound model was proposed to estimate respiratory flow from tracheal sounds. To remove the individual based calibration process, the statistical correlation between the model parameters and anthropometric features of 93 healthy volunteers was investigated. The results show that gender, height and smoking are the most significant factors that affect the model parameters. Hence, a general acoustical flow estimation model was proposed for people with similar height and gender. Second, flow-sound relationship during sleep and wakefulness was studied among 13 OSA patients. The results show that during sleep and wakefulness, flow-sound relationship follows a power law, but with different parameters. Therefore, for acoustical flow estimation during sleep, the model parameters should be extracted from sleep data to have small errors. The results confirm reliability of the acoustical flow estimation for investigating flow variations during both sleep and wakefulness. Finally, a new method for sleep apnea detection and monitoring was developed, which only requires recording the tracheal sounds and the blood's oxygen saturation level (SaO2) data. It automatically classifies the sound segments into breath, snore and noise. A weighted average of features extracted from sound segments and SaO2 signal was used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients. The results show high correlation (0.96,p < 0.0001) between the outcomes of our system and those of the polysomnography. Also, sensitivity and specificity of the proposed method in differentiating simple snorers from OSA patients were found to be more than 91%. These results are superior or comparable with the existing commercialized sleep apnea portable monitors.

Indiana University-Purdue University Indianapolis (IUPUI)
Carbon nanomaterials are widely produced and used in industry, medicine and scientific research. To examine the impact of exposure to nanoparticles on human health, the human airway epithelial cell line, Calu-3, was used to evaluate changes in the cellular proteome that could account for alterations in cellular function of airway epithelia after 24 h exposure to 10 μg/mL and 100 ng/mL of two common carbon nanoparticles, singleand multi-wall carbon nanotubes (SWCNT, MWCNT). After exposure to the nanoparticles, label-free quantitative mass spectrometry (LFQMS) was used to study differential protein expression. Ingenuity Pathway Analysis (IPA) was used to conduct a bioinformatics analysis of proteins identified by LFQMS. Interestingly, after exposure to a high concentration (10 μg/mL; 0.4 μg/cm2) of MWCNT or SWCNT, only 8 and 13 proteins, respectively, exhibited changes in abundance. In contrast, the abundance of hundreds of proteins was altered in response to a low concentration (100 ng/mL; 4 ng/cm2) of either CNT. Of the 281 and 282 proteins that were significantly altered in response to MWCNT or SWCNT, respectively, 231 proteins were the same. Bioinformatic analyses found that the proteins common to both kinds of nanotubes are associated with the cellular functions of cell death and survival, cell-to-cell signaling and interaction, cellular assembly and organization, cellular growth and proliferation, infectious disease, molecular transport and protein synthesis. The decrease in expression of the majority proteins suggests a general stress response to protect cells. The STRING database was used to analyze the various functional protein networks. Interestingly, some proteins like cadherin 1 (CDH1), signal transducer and activator of transcription 1 (STAT1), junction plakoglobin (JUP), and apoptosis-associated speck-like protein containing a CARD (PYCARD), appear in several functional categories and tend to be in the center of the networks. This central positioning suggests they may play important roles in multiple cellular functions and activities that are altered in response to carbon nanotube exposure. To examine the effect of nanotubes on the plasma membrane, we investigated the interaction of short purified MWCNT with model lipid membranes using a planar bilayer workstation. Bilayer lipid membranes were synthesized using neutral 1, 2-diphytanoylsn-glycero-3-phosphocholine (DPhPC) in 1 M KCl. The ion channel model protein, Gramicidin A (gA), was incorporated into the bilayers and used to measure the effect of MWCNT on ion transport. The opening and closing of ion channels, amplitude of current, and open probability and lifetime of ion channels were measured and analyzed by Clampfit. The presence of an intermediate concentration of MWCNT (2 μg/ml) could be related to a statistically significant decrease of the open probability and lifetime of gA channels. The proteomic studies revealed changes in response to CNT exposure. An analysis of the changes using multiple databases revealed alterations in pathways, which were consistent with the physiological changes that were observed in cultured cells exposed to very low concentrations of CNT. The physiological changes included the break down of the barrier function and the inhibition of the mucocillary clearance, both of which could increase the risk of CNT’s toxicity to human health. The biophysical studies indicate MWCNTs have an effect on single channel kinetics of Gramicidin A model cation channel. These changes are consistent with the inhibitory effect of nanoparticles on hormone stimulated transepithelial ion flux, but additional experiments will be necessary to substantiate this correlation.