Computers in biology and medicine
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The aims were to apply a stochastic model to predict outcome early in acute emergencies and to evaluate the effectiveness of various therapies in a consecutively monitored series of severely injured patients with noninvasive hemodynamic monitoring. The survival probabilities were calculated beginning shortly after admission to the emergency department (ED) and at subsequent intervals during their hospitalization. Cardiac function was evaluated by cardiac output (CI), heart rate (HR), and mean arterial blood pressure (MAP), pulmonary function by pulse oximetry (SapO(2)), and tissue perfusion function by transcutaneous oxygen indexed to FiO(2),(PtcO(2)/FiO(2)), and carbon dioxide (PtcCO(2)) tension. ⋯ The CI, SapO(2),PtcO(2)/FiO(2) and MAP were significantly higher in survivors than in nonsurvivors during the initial resuscitation, while HR and PtcCO(2) tensions were higher in the nonsurvivors. Predictions made during the initial resuscitation period in the first 24-hours after admission were compared with the actual outcome at hospital discharge, which were usually several weeks later; misclassifications were 9.6% (16/167). The therapeutic decision support system objectively evaluated the responses of alternative therapies based on responses of patients with similar clinical-hemodynamic states.
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This work describes a model able to synthetize the surface EMG (electromyography) signal acquired from tibialis anterior and gastrocnemious medialis muscles during walking of asymptomatic adult subjects. The model assumes a muscle structure where the volume conductor is represented by multiple layers of anisotropic media. ⋯ The parameters related to the gait cycle, such as onset and cessation timings of muscle activation, amplitude of muscle contraction, periods and sequences of motor units' recruitment, are included in the model presented. In addition, the relative positions of the electrodes during gait can also be specified in order to adapt the simulation to the different acquisition settings.
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A complex nonlinear model for mechanical ventilation, its computer implementation and validation are presented. The model includes the morphometry-based symmetrical structure of the 23 airway generations, dynamic properties of the respiratory system, as well as the description of a ventilator. ⋯ Results of simulations corresponding to normal conditions and airway narrowing are consistent with the published experimental data. The model enables investigations on how specific pathological changes influence the signals and physiological variables during mechanical ventilation, as well as testing known and developing new algorithms tracking time-variability of the respiratory parameters.
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Comparative Study
Automatic detection of erthemato-squamous diseases using adaptive neuro- fuzzy inference systems.
A new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classifiers learned how to differentiate a new case in the domain. ⋯ Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The performances of the ANFIS model were evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.
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The classification problem of respiratory sound signals has been addressed by taking into account their cyclic nature, and a novel hierarchical decision fusion scheme based on the cooperation of classifiers has been developed. Respiratory signals from three different classes are partitioned into segments, which are later joined to form six different phases of the respiration cycle. Multilayer perceptron classifiers classify the parameterized segments from each phase and decision vectors obtained from different phases are combined using a nonlinear decision combination function to form a final decision on each subject. Furthermore a new regularization scheme is applied to the data to stabilize training and consultation.