Inter-hospital transports of critically ill children

There is an increasing need for inter-hospital transports of seriously ill children as a result of centralization of intensive care for children to dedicated pediatric intensive care units (PICU). This thesis has explored the short-and long-term outcomes of critically ill children after trans- port by a specialized pediatric transport team to a PICU. In order to investigate the effects of high altitude on transported children, a novel method was introduced to improve the perfor- mance of a device for regional oxygen saturation (rSO2) monitoring working in a transport environment. In Study I, outcomes of critically ill children acutely transported to PICU by a specialized pediatric transport team were retrospectively compared to a control group of children acutely admitted to the same PICU but through other routes. Transported children were younger, sicker and stayed longer in PICU and their use of PICU specific therapies was higher. The transport per se did not increase the risk of death irrespective of transport distance. Age and risk score related differences in short-and long-term survival among transport- ed patients were evaluated in Study II. Median follow-up time for survivors was 4.4 years. Survival in neonatal patients was high after discharge from intensive care, and patients with a Predicted Death Rate (PDR) > 50% showed no mortality after the 30-day follow up. In con- trast, there was clinically significant late mortality for the whole cohort, especially in those transported multiple times. In Study III we analyzed the impact of an arterial blood gas sample (i.e. the PaO2/FiO2 ratio) on the Pediatric Index of Mortality2 (PIM2) score and its derived probability of death (%). The PIM2 and probability of death only became more accurate if PaO2/FiO2 was available for the respiratory admission group, and when a rather high severity of illness was expected. The aim of Study IV was to investigate the applicability of near-infrared spectroscopy (NIRS) data during transport. The ability to distinguish between real and artefact-related events increased considerably by removal of zero values and “floor-effect values” and then using a filtering technique on the NIRS signal to reduce noise in the signal without loss of the original signal structure. In Study V, rSO2 with NIRS registration from cerebral (rSO2-C) and splanchnic (rSO2-A) areas during air ambulance transports of critically ill children was investigated in relation to the effect of altitude ≥ 5000 feet. Both rSO2-C and rSO2-A decreased significantly at altitude ≥ 5000 feet compared to baseline in a majority of patients. In most patients rSO2-A decreased more than rSO2-C ≥ 5000 feet as expressed by the rSO2-C/rSO2-A ratio, which was > 1 in 67% of patients at baseline and > 1 in 77% of patients at altitude ≥ 5000 feet. In conclusion, this thesis has addressed various aspects of children in need of transport to pediatric intensive care. Acute pediatric inter-hospital transports can be performed with- out increasing the mortality risk regardless of transport distance if performed by a specialized team. There is a notable late mortality after the 30-day follow up for the transported group as a whole. An arterial blood gas sample for the PIM2 score is only needed when the patient has respiratory reason for admission and a rather high severity of illness is expected. Reliable NIRS data can be obtained during transport when cleared for artefacts and smoothed by a noise-reduction algorithm. Both rSO2-C and rSO2-A decreased as an effect of altitude ≥ 5000 feet, however rSO2-C was better preserved than rSO2-

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Análise remota do eletrocardiograma para detecção de eventos isquêmicos

The evolution of technologies for remote services delivery over the Internet unveils a new frontier in the development of the knowledge needed to implement health prevention measures. In this study, a computational tool was conceived for the remote analysis of multiple lead electrocardiograms. As a proof of concept, a method for detecting ST-T segment changes related to ischemic episodes in remote computing is proposed. The architecture combines only open source software that allows incremental object-oriented programming and support multiuser services via the Web, focusing on system evolution within the academic world. The technique used to detect ischemic events favored low computational cost and storage of both data and metadata in a database. It was anchored in a method of interpolation by weighted least squares and histograms, capable of detecting the positions of the QRS complexes, and the respective positions of J points and T waves. These points were used as borderline positions in obtaining representative under curve areas for the subsequent detection of ischemic events in the leads present in the research file. After assessment with engineering students, we conclude that the platform, architecture, and programming techniques provide a satisfactory tool for ischemic event management that can be used to develop new biomedical signal processing techniques that support the risk assessment of myocardial dysfunction.A evolução das tecnologias para entrega de serviços remotos pela Internet revela uma nova fronteira no desenvolvimento do conhecimento necessário para implementar medidas de prevenção da saúde. Neste estudo, uma ferramenta computacional foi concebida para a análise remota de eletrocardiogramas de múltiplas derivações. Como prova de conceito, um método é proposto para detectar alterações no segmento ST-T relacionadas a episódios isquêmicos através da computação remota. A arquitetura combina apenas software de código aberto que permite programação incremental orientada a objetos e oferece suporte a serviços multiusuário via Web, com foco na evolução do sistema no mundo acadêmico. A técnica utilizada para detectar eventos isquêmicos favoreceu o baixo custo computacional e armazenamento de dados e metadados em um Banco de Dados. Foi ancorado em um método de interpolação por mínimos quadrados ponderados e histogramas, capazes de detectar as posições dos complexos QRS e as respectivas posições dos pontos J e ondas T. Esses pontos foram usadas como posições limítrofes na obtenção de áreas representativas sob curvas para a subsequente detecção de eventos isquêmicos nas derivações presentes no arquivo de pesquisa. Após avaliação junto a discentes de engenharia, concluímos que a plataforma, arquitetura e técnicas de programação fornecem uma ferramenta satisfatória para o gerenciamento de eventos isquêmicos, a qual pode ser usada para o desenvolvimento de novas técnicas de processamento de sinais biomédicos que objetivem apoiar a avaliação de risco de disfunção miocárdic

Metodología de implantación de modelos de gestión de la información dentro de los sistemas de planificación de recursos empresariales. Aplicación en la pequeña y mediana empresa

La Siguiente Generación de Sistemas de Fabricación (SGSF) trata de dar respuesta a los requerimientos de los nuevos modelos de empresas, en contextos de inteligencia, agilidad y adaptabilidad en un entono global y virtual. La Planificación de Recursos Empresariales (ERP) con soportes de gestión del producto (PDM) y el ciclo de vida del producto (PLM) proporciona soluciones de gestión empresarial sobre la base de un uso coherente de tecnologías de la información para la implantación en sistemas CIM (Computer-Integrated Manufacturing), con un alto grado de adaptabilidad a la estnictura organizativa deseada. En general, esta implementación se lleva desarrollando hace tiempo en grandes empresas, siendo menor (casi nula) su extensión a PYMEs. La presente Tesis Doctoral, define y desarrolla una nueva metodología de implementación pan la generación automática de la información en los procesos de negocio que se verifican en empresas con requerimientos adaptados a las necesidades de la SGSF, dentro de los sistemas de gestión de los recursos empresariales (ERP), atendiendo a la influencia del factor humano. La validez del modelo teórico de la metodología mencionada se ha comprobado al implementarlo en una empresa del tipo PYME, del sector de Ingeniería. Para el establecimiento del Estado del Arte de este tema se ha diseñado y aplicado una metodología específica basada en el ciclo de mejora continua de Shewhart/Deming, aplicando las herramientas de búsqueda y análisis bibliográfico disponibles en la red con acceso a las correspondientes bases de datos