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DREAM: Diabetic Retinopathy Analysis using Machine Learning

DREAM: Diabetic Retinopathy Analysis using Machine Learning

Diabetic retinopathy (DR) is the leading cause of blindness among people of working age in the developed world. Early screening and detection of DR can prevent about 90% of patients with early signs of DR from acquired blindness. In such situations, automated screening programs using fundus images prior to manual grading can be extremely cost-effective and beneficial. This paper presents a three-stage automated fundus image analysis system called DREAM (Diabetic Retinopathy Analysis using Machine Learning) that detects the presence of mild, moderate and severe DR using fundus images. The proposed system assigns a grade of no DR, mild DR, moderate DR and severe DR, and achieves 100% sensitivity and 53% specificity for screening images with DR vs. healthy fundus images with no DR.

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Wearable Monitoring for Mood Recognition in Bipolar Disorder based on History-Dependent Long-Term Heart Rate Variability Analysis

Wearable Monitoring for Mood Recognition in Bipolar Disorder based on History-Dependent Long-Term Heart Rate Variability Analysis

In this paper, we propose to use a wearable system based on a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire electrocardiogram, respirogram and body posture information in order to detect a pattern of objective physiological parameters to support the diagnosis of bipolar disorders. Moreover, we implemented a novel ad-hoc methodology of advanced biosignal processing able to effectively recognize four possible clinical mood states in bipolar patients continuously monitored up to 18 hours. Experimental results demonstrate that our novel clinical decision support system for bipolar disorders can recognize mood states with classification accuracy up to 95.81%.

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Brain Computer Interface Classifier for Wheelchair Commands using Neural Network with Fuzzy Particle Swarm Optimization

Brain Computer Interface Classifier for Wheelchair Commands using Neural Network with Fuzzy Particle Swarm Optimization

This paper presents the classification of a three-class mental task-based brain computer interface (BCI) that uses the Hilbert-Huang transform (HHT) for the features extractor and fuzzy particle swarm optimization with cross mutated-based artificial neural network (FPSOCM-ANN) for the classifier. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The FPSOCM-ANN provides improved accuracies compared to genetic algorithm-based artificial neural network (GA-ANN) for three mental tasks-based BCI classifications with the best classification accuracy achieved for a 7s time-window: 84.4% (FPSOCM-ANN) compared to 77.4% (GA-ANN).

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Logic-Centered Architecture for Ubiquitous Health Monitoring

Logic-Centered Architecture for Ubiquitous Health Monitoring

This paper presents such a generic architecture for multi-parametric, intelligent and ubiquitous wireless sensing platforms. It is a transparent, smartphone-based sensing framework with customizable wireless interfaces and plug‘n’play capability to easily interconnect third party sensor devices. It caters to wireless body (BAN), personal (PAN), and near-me (NAN) area networks. In this paper, the new logic-centered software architecture for ubiquitous health monitoring applications is described, followed by a discussion as to how it helps to shift focus from software and hardware development, to medical and health process-centered design of new SWS applications.

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About This Journal

IEEE Journal of Biomedical and Health Informatics (J-BHI) publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine. Papers must contain original content in theoretical analysis, methods, technical development, and/or novel clinical applications of information systems.

Retitled from the IEEE Transactions on Information Technology in Biomedicine (T-ITB) in 2013, the J-BHI is one of the leading journals in computer science and information systems with a strong interdisciplinary focus and biomedical and health application emphasis. Topics covered by J-BHI include, but are not limited to: acquisition, transmission, storage, retrieval, management, processing and analysis of biomedical and health information; applications of information and communication technologies to the practice of healthcare, personal well-being, preventive care and early diagnosis of diseases, and discovery of new therapies and patient specific treatment protocols; and integration of electronic medical and health records, methods of longitudinal data analysis, data mining and knowledge discovery tools.

Manuscripts may deal with these applications and their integration, such as clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body senor networks, informatics in biological and physiological systems, personalized and pervasive health technologies (telemedicine, u-, p-, m- and e-Health) for public health, home healthcare and wellness management. Topics related to integration include interoperability, protocol-based patient care, evidence-based medicine, and methods of secure patient data.

Papers published by J-BHI are typically organised under section headings of Bioinformatics, Imaging Informatics, Sensor Informatics, Medical Informatics, and Public Health Informatics. These are complemented by managed special issues/sections covering topics that are of strategic importance to the journal, coordinated by guest editors who are leading experts in these fields. We particularly encourage large cohort studies with clearly demonstrated clinical translational values supplemented by online data sets or algorithms that can be shared by the research community.

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