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

Análisis de la interacción cardíaca y respiratoria en pacientes con cardiomiopatía y pacientes en proceso de extubación

Study and evaluation of patients with heart failure related with ischemic and dilated cardiomyopathy is interesting for the clinical practice. Similarly, the analysis of the behavior of the respiratory system of patients undergoing extubation process is still research topic, and is particularly important to determine the optimal moment for the weaning process. In Both cases, the study of the cardiac and respiratory systems, and their interaction can contribute to identify new medical process, and increase the knowledge of the pathologies studied. The main goal of this research is the analysis and characterization of the behavior of the cardiovascular system, the respiratory system, and the cardiorespiratory interaction in patients with chronic heart failure, and patients assisted by mechanical ventilation on the processes of weaning trials. We propose the extraction of information that allows to enhance the knowledge of the physiological behavior, and improve the diagnosis and stratification of these patients. Cardiomyopathy is a disease of the cardiac muscle reducing the heart’s pumping ability. Patients with chronic heart failure (CHF) usually present changes in the respiratory behavior, with periodical changes in the tidal volume, known as periodic breathing (PB). In order to study chronic heart failure patients, we propose to analyse the cardiorespiratory interaction through characteristics extracted, in time and frequency domain, from the respiratory flow (FLW), respiratory volume (VOL), electrocardiogram ECG, and blood pressure (BP) signals. The study was performed in 50 patients with dilated cardiomyopathy (19 patients), and ischemic cardiomyopathy (31 patients). A periodic respiratory pattern is considered as an indicator of the severity of these disease. Using the respiratory flow envelope, we calculated the modulation index (M) associated to the periodic breathing. First part of the study considered CHF patients classified based on the modulation index. The study was performed considering 35 segments of signals non modulated (GN, M75%). Considering these time segments, we studied parameters extracted from the cardiac, respiratory and cardiorespiratory interaction related to the envelope of the respiratory flow signal. The main objective is to analyze differences on the cardiovascular behavior between non modulated and modulated patients. Studying respiratory signals, the main differences were found on the end expiratory lung volume (EELV). Considering the parameters extracted from the ECG, were obtained difference on the spectrum of the upward and downward slope of the QRS complex. Differences were also found on the heart rate variability. From the analysis of the magnitude squared coherence (MSC) between the series and the envelope the main differences were found in the frequency domain on the band of very low frequency. This result suggest that the periodic changes on the tidal volume are presented in the cardiac behavior. CHF patients were also studied through an unsupervised classification based on the K-means method. Time and frequency features extracted from the series were used to perform a 2 clusters classification. Based on these cluster were analyzed the clinical information. Additional, were study the modulation index on these clusters. Results suggest a strong correlation between feature extracted form ECG series and the modulation of the respiratory. Clusters calculated form BP series are related with the blood volume on the ventricle. Mechanical ventilation guarantee a correct alveolar ventilation in patients with respiratory failure. Weaning trial is the process of transfer the respiratory effort from the mechanical ventilator to the patient. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GS), 33 failed to maintain spontaneous breathing so were reconnected (GF), and 15 were extubated after the test but reintubated within 48 h (GR). The power spectral density and MSC of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) over the spectral signals. Considering this new space formed by the PCA were calculated a fuzzy K-nearest neighbor classification. Best classification index, applying PCA and fKNN methods, were obtained considering the spectral signal of the MSC between HRV and FLW. The classifiers present a good balance between sensibility and specificity, and high accuracy of 92% comparing GS vs. GF, 86% classifying GS vs. GR, and 83% classifying GS vs. GFR. Reintubated patients is the most complex group for the analysis and classification, because at the beginning of the trial the behavior is similar to the success patients, but before 48 h the evolution of the respiratory pattern is more comparable to the failure patients. However, applying our method the index of accuracy, sensibility and specificity comparing GR against the other groups are above 80%. Spectral analysis of weaning trial patients were complemented with a nonlinear study based on the recurrence plot (RP) method. Additional, to the individual RP analysis of the HRV series, inspiratory time series (TI ), and respiratory time series (TTot), were performed the study of the cardiorespiratory interaction through the cross recurrence plot (CRP) and joint recurrence plot (JRP). One of the main issues for the application of the recurrence technique is the correct selection of the embedding dimension (ε). We propose the application of the method based on the analysis of ROC curves comparing observational noise against the signals of the study. This method allow the selection of the optimal ε for each data series. Recurrence matrix where characterized based on the parameters extracted applying the recurrence quantification analysis (RQA). Comparing the different groups that the main differences was observed on the determinism and stationarity of the signals. Patients of the success group show higher values of determinism, suggesting that time series extracted form de GE patients during the weaning trials are more stationary comparing with the orders groups. Analyzing the result obtained from JRP, it concludes that patients from GS show a higher mutual recurrence between HRV and the time series (TI and (TTot. Based on the RQA parameters obtained for each time series extracted, was proposed a supervised classification applying support vector machine technique (SVM). The best classification obtained were abode the 80% in accuracy. The results obtained suggest that recurrence plot and especially joint recurrence plot techniques could improve the discrimination and characterization of patients on weaning trialsEl estudio y la evaluación de pacientes con problemas cardíacos relacionados con cardiomiopatía isquémica o dilatada son de gran interés para la práctica clínica. Igualmente, el análisis del comportamiento del sistema respiratorio de pacientes en proceso de extubación continúa siendo tema de investigación, y un reto en la práctica clínica. En ambos casos, el estudio de los sistemas cardíaco y respiratorio, y de su interacción cardiorespiratoria pueden contribuir a definir nuevos procesos clínicos, y a un mayor conocimiento de las patologías estudiadas. Pacientes con fallo cardíaco crónico (CHF) a menudo presentan aumentos y disminuciones periódicas en el volumen de tidal. Para el estudio de estos pacientes se propone el análisis de la interacción cardiorespiratoria, a través de la caracterización en el dominio temporal y frecuencial de las señales de flujo (FLW) y volumen (VOL) respiratorio, la señal ECG y la señal de presión sanguínea (BP). Se analizaron 50 pacientes diagnosticados con cardiomiopatía dilatada (19 pacientes), y cardiomiopatía isquémica (31 pacientes). A partir de la envolvente de la señal FLW se calculó el índice de modulación (M) asociado con el comportamiento periódico de la respiración. Para el estudio fueron considerados 35 segmentos de señal no modulada (GN, M75%). Las principales diferencias se encontraron al analizar los parámetros relacionados con el volumen pulmonar al final de la espiración, los valores espectrales de las pendientes de subida y de bajada del complejo QRS, y la variabilidad del ritmo cardíaco (HRV). El análisis de la magnitud de la coherencia al cuadrado (MSC) entre las series extraídas y la envolvente de la señal FLW presentó las principales diferencias en la banda de muy baja frecuencia (VLF), con valores más elevados en los pacientes del grupo GH. Teniendo en cuenta los parámetros temporales y espectrales de las series extraídas de las señales cardiovascular y respiratoria se realizó una clasificación de los pacientes CHF aplicando el método no supervisado K-means. Al analizar las señales de presión sanguínea se observa una marcada correlación entre los clusters formados por el clasificador y los parámetros de volumen de sangre en los ventrículos. El destete es el proceso de transferencia del trabajo respiratorio desde el ventilador mecánico al paciente. Se propone evaluar las componentes espectrales de la HRV, de la señal FLW, y de su coherencia espectral para la identificación de pacientes exitosos (GE) en el test de respiración espontánea, pacientes que fracasan (GF) en el proceso de destete, y pacientes que habiendo superado la prueba inicial de respiración espontánea, antes de 48 h tuvieron que ser reintubados y reconectados al ventilador mecánico (GR). Para este estudio se analizaron las señales ECG y FLW de 121 pacientes asistidos mediante ventilación mecánica y sometidos a la prueba de tubo en T para la extubación. La caracterización del comportamiento cardiorespiratorio se realizó aplicando un análisis de componentes principales (PCA) a los espectros de las señales. En particular, se estudió la magnitud de la coherencia al cuadrado (MSC) como medida espectral del acople entre las señales HRV y FLW. Los pacientes fueron clasificados aplicando la técnica fuzzy K vecinos más cercanos (fKNN). Los mejores índices de clasificación se obtuvieron al considerar la señal espectral de la MSC entre la HRV y FLW, obteniéndose clasificaciones superiores al 80%. El estudio espectral de los pacientes en proceso de extubación se complementó con un estudio no lineal basado en la técnica de recurrence plot (RP). Adicionalmente, al estudio individual de las series de HRV, los tiempos de inspiración (Ti), y los tiempos de duración del ciclo respiratorio (TTot), se realizó el análisis de su interacción aplicando el método de joint recurrence plot (JRP). Pacientes del grupo GE presentaron valores más elevados al compararlos con los otros grupos

Quality framework for semantic interoperability in health informatics: definition and implementation

Aligned with the increased adoption of Electronic Health Record (EHR) systems, it is recognized that semantic interoperability provides benefits for promoting patient safety and continuity of care. This thesis proposes a framework of quality metrics and recommendations for developing semantic interoperability resources specially focused on clinical information models, which are defined as formal specifications of structure and semantics for representing EHR information for a specific domain or use case. This research started with an exploratory stage that performed a systematic literature review with an international survey about the clinical information modelling best practice and barriers. The results obtained were used to define a set of quality models that were validated through Delphi study methodologies and end user survey, and also compared with related quality standards in those areas that standardization bodies had a related work programme. According to the obtained research results, the defined framework is based in the following models: Development process quality model: evaluates the alignment with the best practice in clinical information modelling and defines metrics for evaluating the tools applied as part of this process. Product quality model: evaluates the semantic interoperability capabilities of clinical information models based on the defined meta-data, data elements and terminology bindings. Quality in use model: evaluates the suitability of adopting semantic interoperability resources by end users in their local projects and organisations. Finally, the quality in use model was implemented within the European Interoperability Asset register developed by the EXPAND project with the aim of applying this quality model in a broader scope to contain any relevant material for guiding the definition, development and implementation of interoperable eHealth systems in our continent. Several European projects already expressed interest in using the register, which will now be sustained by the European Institute for Innovation through Health Data