An informatics based approach to respiratory healthcare.

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM

Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging

An informatics based approach to respiratory healthcare

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Breathing pattern characterization in patients with respiratory and cardiac failure

El objetivo principal de la tesis es estudiar los patrones respiratorios de pacientes en proceso de extubación y pacientes con insuficiencia cardiaca crónica (CHF), a partirde la señal de flujo respiratorio. La información obtenida de este estudio puede contribuir a la comprensión de los procesos fisiológicos subyacentes,y ayudar en el diagnóstico de estos pacientes. Uno de los problemas más desafiantes en unidades de cuidados intensivos es elproceso de desconexión de pacientes asistidos mediante ventilación mecánica. Más del 10% de pacientes que se extuban tienen que ser reintubados antes de 48 horas. Una prueba fallida puede ocasionar distrés cardiopulmonar y una mayor tasa de mortalidad. Se caracterizó el patrón respiratorio y la interacción dinámica entre la frecuenciacardiaca y frecuencia respiratoria, para obtener índices no invasivos que proporcionen una mayor información en el proceso de destete y mejorar el éxito de la desconexión.Las señales de flujo respiratorio y electrocardiográfica utilizadas en este estudio fueron obtenidas durante 30 minutos aplicando la prueba de tubo en T. Se compararon94 pacientes que tuvieron éxito en el proceso de extubación (GE), 39 pacientes que fracasaron en la prueba al mantener la respiración espontánea (GF), y 21 pacientes quesuperaron la prueba con éxito y fueron extubados, pero antes de 48 horas tuvieron que ser reintubados (GR). El patrón respiratorio se caracterizó a partir de las series temporales. Se aplicó la dinámica simbólica conjunta a las series correspondientes a las frecuencias cardiaca y respiratoria, para describir las interacciones cardiorrespiratoria de estos pacientes. Técnicas de "clustering", ecualización del histograma, clasificación mediante máquinasde soporte vectorial (SVM) y técnicas de validación permitieron seleccionar el conjunto de características más relevantes. Se propuso una nueva métrica B (índice de equilibrio) para la optimización de la clasificación con muestras desbalanceadas. Basado en este nuevo índice, aplicando SVM, se seleccionaron las mejores características que mantenían el mejor equilibrio entre sensibilidad y especificidad en todas las clasificaciones. El mejor resultado se obtuvo considerando conjuntamente la precisión y el valor de B, con una clasificación del 80% entre los grupos GE y GF, con 6 características. Clasificando GE vs. el resto de los pacientes, el mejor resultado se obtuvo con 9 características, con 81%. Clasificando GR vs. GE y GR vs. el resto de pacientes la precisión fue del 83% y 81% con 9 y 10 características, respectivamente. La tasa de mortalidad en pacientes con CHF es alta y la estratificación de estospacientes en función del riesgo es uno de los principales retos de la cardiología contemporánea. Estos pacientes a menudo desarrollan patrones de respiraciónperiódica (PB) incluyendo la respiración de Cheyne-Stokes (CSR) y respiración periódica sin apnea. La respiración periódica en estos pacientes se ha asociadocon una mayor mortalidad, especialmente en pacientes con CSR. Por lo tanto, el estudio de estos patrones respiratorios podría servir como un marcador de riesgo y proporcionar una mayor información sobre el estado fisiopatológico de pacientes con CHF. Se pretende identificar la condición de los pacientes con CHFde forma no invasiva mediante la caracterización y clasificación de patrones respiratorios con PBy respiración no periódica (nPB), y patrón de sujetos sanos, a partir registros de 15minutos de la señal de flujo respiratorio. Se caracterizó el patrón respiratorio mediante un estudio tiempo-frecuencia estacionario y no estacionario, de la envolvente de la señal de flujo respiratorio. Parámetros relacionados con la potencia espectral de la envolvente de la señal presentaron losmejores resultados en la clasificación de sujetos sanos y pacientes con CHF con CSR, PB y nPB. Las curvas ROC validan los resultados obtenidos. Se aplicó la "correntropy" para una caracterización tiempo-frecuencia mas completa del patrón respiratorio de pacientes con CHF. La "corretronpy" considera los momentos estadísticos de orden superior, siendo más robusta frente a los "outliers". Con la densidad espectral de correntropy (CSD) tanto la frecuencia de modulación como la dela respiración se representan en su posición real en el eje frecuencial. Los pacientes con PB y nPB, presentan diferentesgrados de periodicidad en función de su condición, mientras que los sujetos sanos no tienen periodicidad marcada. Con único parámetro se obtuvieron resultados del 88.9% clasificando pacientes PB vs. nPB, 95.2% para CHF vs. sanos, 94.4% para nPB vs. sanos.The main objective of this thesis is to study andcharacterize breathing patterns through the respiratory flow signal applied to patients on weaning trials from mechanicalventilation and patients with chronic heart failure (CHF). The aim is to contribute to theunderstanding of the underlying physiological processes and to help in the diagnosis of these patients. One of the most challenging problems in intensive care units is still the process ofdiscontinuing mechanical ventilation, as over 10% of patients who undergo successfulT-tube trials have to be reintubated in less than 48 hours. A failed weaning trial mayinduce cardiopulmonary distress and carries a higher mortality rate. We characterize therespiratory pattern and the dynamic interaction between heart rate and breathing rate toobtain noninvasive indices that provide enhanced information about the weaningprocess and improve the weaning outcome. This is achieved through a comparison of 94 patients with successful trials (GS), 39patients who fail to maintain spontaneous breathing (GF), and 21 patients who successfully maintain spontaneous breathing and are extubated, but require thereinstitution of mechanical ventilation in less than 48 hours because they are unable tobreathe (GR). The ECG and the respiratory flow signals used in this study were acquired during T-tube tests and last 30 minute. The respiratory pattern was characterized by means of a number of respiratory timeseries. Joint symbolic dynamics applied to time series of heart rate and respiratoryfrequency was used to describe the cardiorespiratory interactions of patients during theweaning trial process. Clustering, histogram equalization, support vector machines-based classification (SVM) and validation techniques enabled the selection of the bestsubset of input features. We defined a new optimization metric for unbalanced classification problems, andestablished a new SVM feature selection method, based on this balance index B. The proposed B-based SVM feature selection provided a better balance between sensitivityand specificity in all classifications. The best classification result was obtained with SVM feature selection based on bothaccuracy and the balance index, which classified GS and GFwith an accuracy of 80%, considering 6 features. Classifying GS versus the rest of patients, the best result wasobtained with 9 features, 81%, and the accuracy classifying GR versus GS, and GR versus the rest of the patients was 83% and 81% with 9 and 10 features, respectively.The mortality rate in CHF patients remains high and risk stratification in these patients isstill one of the major challenges of contemporary cardiology. Patients with CHF oftendevelop periodic breathing patterns including Cheyne-Stokes respiration (CSR) and periodic breathing without apnea. Periodic breathing in CHF patients is associated withincreased mortality, especially in CSR patients. Therefore it could serve as a risk markerand can provide enhanced information about thepathophysiological condition of CHF patients. The main goal of this research was to identify CHF patients' condition noninvasively bycharacterizing and classifying respiratory flow patterns from patients with PB and nPBand healthy subjects by using 15-minute long respiratory flow signals. The respiratory pattern was characterized by a stationary and a nonstationary time-frequency study through the envelope of the respiratory flow signal. Power-related parameters achieved the best results in all of the classifications involving healthy subjects and CHF patients with CSR, PB and nPB and the ROC curves validated theresults obtained for the identification of different respiratory patterns. We investigated the use of correntropy for the spectral characterization of respiratory patterns in CHF patients. The correntropy function accounts for higher-order moments and is robust to outliers. Due to the former property, the respiratory and modulationfrequencies appear at their actual locations along the frequency axis in the correntropy spectral density (CSD). The best results were achieved with correntropy and CSD-related parameters that characterized the power in the modulation and respiration discriminant bands, definedas a frequency interval centred on the modulation and respiration frequency peaks,respectively. All patients, i.e. both PB and nPB, exhibit various degrees of periodicitydepending on their condition, whereas healthy subjects have no pronounced periodicity.This fact led to excellent results classifying PB and nPB patients 88.9%, CHF versushealthy 95.2%, and nPB versus healthy 94.4% with only one parameter.Postprint (published version

Implementation of a 3D CNN for COPD classification

Segons les prediccions de la Organització Mundial de la Salut (OMS) pels voltants del 2030 la Malaltia Pulmonar Obstructiva Crònica (MPOC) es convertirá en la tercera causa de mort en tot el món. L’MPOC és una patologia que afecta a les vies respiratòries i als pulmons. Avui en dia esdevé crónica i incurable però, és una malaltia tractable i prevenible. Fins ara les proves de diagnòstic usades per a detectar l’MPOC es basen en l’espirometria, aquesta prova, tot i indicar el grau d’obstrucció al pas de l’aire que es produeix en els pulmons, sovint no és molt fiable. És per aquest motiu que s’estan començant a usar tècniques basades en algorismes de Deep Learning per a la classificaió més acurada d’aquesta patologia, basant-se en imatges tomogràfiques de pacients malalts d’MPOC. Les xarxes neuronals convolucionals en tres dimensions (3D-CNN) en són un exemple. A partir de les dades i les imatges obtingudes en l’estudi observacional d’ECLIPSE proporcionades per l’equip de recerca de BRGE de ISGlobal, s’implementa una 3D-CNN per a la classificació de pacients amb risc d’MPOC. Aquest treball té com a objectiu desenvolupar una recerca extensa sobre la recerca actual en aquest àmbit i proposa millores per a l’optimització i reducció del cost computacional d’una 3D-CNN per aquest cas d’estudi concret.Según las predicciones de la Organización Mundial de la Salud (OMS), para alrededor del 2030, la Enfermedad Pulmonar Obstructiva Crónica (EPOC) se convertirá en la tercera causa de muerte en todo el mundo. La EPOC es una enfermedad que afecta las vías respiratorias y los pulmones. En la actualidad, se considera crónica e incurable, pero es una enfermedad tratable y prevenible. Hasta ahora, las pruebas de diagnóstico utilizadas para detectar la EPOC se basan en la espirometría. Esta prueba, a pesar de indicar el grado de obstrucción en el flujo de aire que ocurre en los pulmones, a menudo no es muy confiable. Es por esta razón que se están empezando a utilizar técnicas basadas en algoritmos de Deep Learning para una clasificación más precisa de esta patología, utilizando imágenes tomográficas de pacientes enfermos de EPOC. Las redes neuronales convolucionales en tres dimensiones (3D-CNN) son un ejemplo de esto. A partir de los datos y las imágenes obtenidas en el estudio observacional ECLIPSE proporcionado por el equipo de investigación de BRGE de ISGlobal, se implementa una 3D-CNN para la clasificación de pacientes con riesgo de EPOC. Este trabajo tiene como objetivo desarrollar una investigación exhaustiva sobre la investigación actual en este campo y propone mejoras para la optimización y reducción del costo computacional de una 3D-CNN para este caso de estudio concreto.According to predictions by the World Health Organization (WHO), by around 2030, Chronic Obstructive Pulmonary Disease (COPD) will become the third leading cause of death worldwide. COPD is a condition that affects the respiratory tract and lungs. Currently, it is considered chronic and incurable, but it is a treatable and preventable disease. Up to now, diagnostic tests used to detect COPD have been based on spirometry. Despite indicating the degree of airflow obstruction in the lungs, this test is often not very reliable. That is why techniques based on Deep Learning algorithms are being increasingly used for more accurate classification of this pathology, based on tomographic images of COPD patients. Three-dimensional Convolutional Neural Networks (3D-CNN) are an example of such techniques. Based on the data and images obtained in the observational study called ECLIPSE, provided by the research team at BRGE of ISGlobal, a 3D-CNN is implemented for the classification of patients at risk of COPD. This work aims to conduct extensive research on the current state of research in this field and proposes improvements for the optimization and reduction of the computational cost of a 3D-CNN for this specific case study

Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

Automation through Deep-Learning to Quantify Ventilation Defects in Lungs from High-Resolution Isotropic Hyperpolarized 129Xe Magnetic Resonance Imaging

Obstructive lung diseases are characterized by heterogenous ventilation. Hyperpolarized 129Xe gas lung magnetic resonance imaging (MRI) can examine lung ventilation heterogeneity by acquiring high-resolution isotropic images. The current gold standard of semi-automated (SA) segmentation can be used to quantify non-isotropic 129Xe lung images to generate ventilation defect percent (VDP), however, this method is not suitable for analysis of isotropic voxel 129Xe images due to the large number of slices. Therefore, we used a fully automated deep learning-based (DL) lung algorithm to calculate VDP from isotropic images. SNR, SA and DL-based VDP were calculated, showing a strong positive linear correlation with a zero intercept and close to unity slope. This study demonstrates the feasibility of using DL-based segmentation methods to quantify ventilation defects, which has potential for clinical translation of 129Xe MRI as a tool for treatment and monitoring for patients with pulmonary diseases

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 402)

This bibliography lists 244 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Nov. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo