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Abstract Introduction According to the World Health Organization, about 9.2% of the 28 million newborns worldwide are stillborn. Besides, about 358,000 women died due to complications related to pregnancy in 2015. Part of these deaths could have been avoided with improving prenatal care agility to recognize problems during pregnancy. Based on that, many efforts have been made to provide technologies that can contribute to offer better access to information and assist in decision-making. In this context, this work presents an architecture to automate the classification and referral process of pregnant women between the basic health units and the referral hospital through a Telehealth platform. Methods The Telehealth architecture was developed in three components: The data acquisition component, responsible for collecting and inserting data; the data processing component, which is the core of the architecture implemented using expert systems to classify gestational risk; and the post-processing component, in charge of the delivery and analysis of cases. Results Acceptance test, system accuracy test based on rules and performance test were realized. For the tests, 1,380 referral forms of real situations were used. Conclusion On the results obtained with the analysis of real data, ILITIA, the developed architecture has met the requirements to assist medical specialists on gestational risk classification, which decreases the inconvenience of pregnant women displacement and the resulting costs.
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Introduction: The communication of information systems with biomedical devices has become complex not only due to the existence of several private communication protocols, but also to the immutable way that software is embedded into these devices. In this sense, this paper proposes a service-oriented architecture to access biomedical devices as a way to abstract the mechanisms of writing and reading data from these devices, thus contributing to enable the focus of the development team of biomedical software to be intended for its functional requirements, i.e. business rules relevant to the problem domain. Methods The SOA-BD architecture consists of five main components: A Web Service for transport and conversion of the device data, Communication Protocols to access the devices, Data Parsers to preprocess data, a Device Repository to store data and transmitted information and Error handling, for error handling of these information. For the development of SOA-BD, technologies such as the XML language and the Java programming language were used. Besides, Software Engineering concepts such as Design Patterns were also used. For the validation of this work, data has been collected from vital sign monitors in an Intensive Care Unit using HL7 standards. Results The tests obtained a difference of about only 1 second in terms of response time with the use of SOA-BD. Conclusion SOA-BD achieves important results such as the reduction on the access protocol complexity, the opportunity for treating patients over long distances, allowing easier development of monitoring applications and interoperability with biomedical devices from diverse manufacturers.
ABSTRACT
Uma identificação correta de transientes em sinais de ECG (Eletrocardiograma) pode auxiliar métodos de processamento de sinal de ECG, pois esse tipo de evento degrada o sinal e pode induzir a erros. Diante disso, o presente trabalho propõe uma arquitetura para a detecção desses fenômenos, seguindo a tendência atual da computação distribuída, na qual um sensor realiza a detecção dos transientes no momento da aquisição do sinal, e, em seguida, encaminha essa informação através de uma rede de comunicação de dados, desenvolvida especialmente para a automação hospitalar, até um dispositivo computacional que irá processar os dados ou então apresentá-los a um profissional capacitado para fazer a análise de forma manual. Para realizar a detecção de transientes, foi proposto um método matemático baseado na transformada Hilbert do sinal de ECG, aliado ao PM-AH (Protocolo Multiciclos para Automação Hospitalar), com adição de quadros neste, para que seja possível o envio da informação sobre a ocorrência de transientes junto aos dados do sinal de eletrocardiograma. Dentre os transientes possíveis, foi escolhido o ruído, por ser o fenômeno que mais interfere no processamento de sinais de ECG, onde testes foram realizados com a base de dados MIT-BIH Arrhythmia Database, enquanto uma análise matemática foi feita nos novos quadros do protocolo PM-AH, com o intuito de demonstrar a consistência do protocolo com esta adição.
A correct identification of transients in the ECG (electrocardiogram) can assist processing methods for ECG signals, since this type of event degrades the signal and can be misleading. Therefore, this paper proposes an architecture for detection of these phenomena, following the current trend of distributed computing, in which a sensor will detect transients at the time of signal acquisition, and then forward this information through a data communication network, designed specifically for hospital automation, to a computing device that will process the data or present it to a trained professional for manual analysis. To perform the detection of transients, a mathematical method based on the Hilbert transform of the ECG signal is proposed here, allied with the MP-HA (Multicycle Protocol for Hospital Automation), with the addition of frames, so that information on the occurrence of transients can be transmitted along with signal data of the electrocardiogram. Among the possible transients, noise was chosen because it is the phenomenon that interferes the most with the processing of ECG signals. Tests were performed using the MIT-BIH Arrhythmia Database, while a mathematical analysis was used in the new frames of the MP-HA protocol in order to demonstrate the consistency of the protocol with this addition.