Computers in biology and medicine
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Noninvasive mechanical ventilation is an effective procedure to manage patients with acute or chronic respiratory failure. Most ventilators act as flow generators that assist spontaneous respiratory cycles by delivering inspiratory and expiratory pressures. This allows the patient to improve alveolar ventilation and subsequent pulmonary gas exchanges. ⋯ Phase synchronization can be identified from phase portrait and the breath-to-breath variability is well characterized by return maps. This paper is a first step in the direction of automatically estimating the comfort from measurements and not from a necessarily subjective answer given by the patient. These tools could be helpful for the physicians to set the ventilator parameters.
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This work presents an important part of our telemedicine system for critical care units: a distributed module based on intelligent agents technology that is dedicated to the process management of a network for medical monitoring, including distribution and control of processing tasks and bandwidth management. The system provides the real-time acquisition and analysis of physiological data, the graphical visualisation of these data, and their transmission to a central system charged with the collection and control of all the information concerning the patient, including knowledge-based systems (KBS) for medical reasoning.
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A multi-scale computational method, which combines a lumped parameter model of the cardiovascular system (CVS) with a three-dimensional (3D) left ventricle (LV) hemodynamic solver, is developed for quantitatively evaluating the LV function. The parameter model allows reasonable predictions of the cardiac variables in a closed-loop manner under both normal and various pathological conditions. On the basis of the parameter-model-predicted results, 3D hemodynamic computations further provide quantitative insights into the detailed intraventricular flow patterns. Based on a series of computations, it is demonstrated that the pathological change in the shape and size of the LV has a significant effect on the LV pumping performance.
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Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. ⋯ The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.
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Comparative Study
A dynamic fuzzy neural filter for separation of discontinuous adventitious sounds from vesicular sounds.
This paper presents a recurrent filter that performs real-time separation of discontinuous adventitious sounds from vesicular sounds. The filter uses two Dynamic Fuzzy Neural Networks, operating in parallel, to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. Extensive experimental results, including fine/coarse crackles and squawks, are given, and a performance comparison with a series of other models is conducted, underlining the separation capabilities of the proposed filter and its improved performance with respect to its competing rivals.