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The novel hybrid (pneumo-electrical-numerical physical) model of lungs is presented. A general procedure of creating of this type of models is also described. It consists in application of proportional transformation of an electrical impedance of a lumped parameter electrical or numerical model of lungs into a pneumatic impedance obtained in an input pneumatic terminal of the model. The standard Dubois mathematical model of lungs has been applied in model examinations. That proved the assumed concept of hybrid modeling.

Congenital nystagmus (CN) is a disturbance of the oculomotor centers which develops at birth or in the first months of life. Nystagmus consists essentially in involuntary, conjugated, horizontal rhythmic movements of the eye. Its pathogenesis is still unknown. Current therapies for CN aim to increase the patient’s visual acuity by means of refraction defects correction, drug delivery and ocular muscle surgery. Eye movement recording supports for accurate diagnosis, for patient follow-up and for therapy evaluation. In general, CN patients show a considerable decrease of visual acuity (image fixation on the retina is obstructed by nystagmus continuous oscillations) and severe postural alterations such as the anomalous head position, searched by patient to obtain a better fixation of the target image onto the retina. Often CN presents ‘neutral zones’ corresponding to particular gaze angles, in which nystagmus amplitude minimizes allowing a longer foveation time and a more stable repositioning of foveations, increasing visual acuity. Selected patients’ eye movements were recorded by using EOG or infrared oculography devices. Visual stimulation was delivered by means of an arched LED bar covering a visual field of –30 +30 degrees with respect to the central position. Computation of CN concise parameters allows in-dept analysis of foveations and estimation of visual acuity at different gaze angles. Preliminary results show a maximum of visual acuity at a specific gaze angle; this angle is mostly located at the patient’s right side for the analyzed group.

Continuous blood pressure recording carries the most information on the cardiovascular state of a person. Therefore accurate instrumentation is of high importance. Nowadays the most accurate continuous blood pressure measuring method is the intra-arterial catheterization. However, the accuracy of the fluid-filled catheters raises doubts: the elastic wall of the catheter and the transmission tube each has a damping effect that could play a significant role together. Furthermore the intra-arterial part of a cardiac catheter is in a pulsatile flow, which is assumed to affect the pressure transmission within the measuring line. In this paper behavior of two different types of fluid filled catheters (femoral and cardiac) is described. For the in vitro experiments, a pulsatile arterial system model was applied. Simultaneous measurements of the intra-arterial pressure were carried out: directly with use of a pressure transmitter and through the catheter. Thus the accuracy and the frequency response of the catheters could be obtained and a comparison between the two different types could be made. A numerical model – based on the method of impedances – was developed to describe the frequency transmitting ability of the catheters. The numerical results were compared to the measurements. We found that the experimental results of the different catheters show significant similarities; the numerical and experimental results of the femoral catheter were in a good accordance whereas those of the cardiac catheter show discrepancies.

To estimate blood pressure with using pulse wave transit time method, the PPG and ECG signals have to be measured with high quality. This paper describes a device that improves PPG signal quality, with using different analogue and digital signal processing methods. The device is developed for the 24-hour ambulatory blood pressure monitoring system. Part of the device is designed in hardware and part of it is modelled in MATLAB. The experiments with PPG signal, noises and DC component drift included, have been carried out. As a result, the PPG signal quality has been improved with this device.

The method of artificial neural networks was applied for analysis of the data obtained in the clinical trials of the optical biopsy system. Detection of malignant tissue sections was carried out using a multilayer perceptron. The coefficients of wavelet decomposition of optical scattering spectra were given at the perceptron input and its output gave the malignancy probability for the current spectrum. End-to-end probability calculation throughout the optical biopsy procedure dataset showed reliable detection of the cancer sections in the same place as it was specified by experts.

Kinematic analysis of handwriting is a promising new frontier towards the characterization of handwriting movements, both in children and adults, with and without difficulties or pathologies that disrupt normal handwriting processes. The challenge, however, is to define and measure parameters that tap and highlight underlying mechanisms and strategies in order to comprehend such disorders, promote prevention programs and provide treatment or remedy, whenever possible. This work represents a practical application, binding neuropsychologic theory and engineering technology in the development of a device that enables on-line process analysis of handwriting, offering ample possibilities of research, both in medical and educational fields. Although employing complex mathematical procedures, the device is user friendly in its interface design and allows the rapid analysis of the parameters reported in literature, as well as some new and interesting variables that may contribute to the understanding of handwriting difficulties. The device has been successfully tested and used in a major Italian institute for childcare and research to evaluate handwriting proficiency in children. Preliminary results indicate that kinematic analysis of handwriting thus performed provides important information for the diagnosis and treatment of dysgraphia.

Attention domain is of crucial importance for goal-directed behaviors and it has been widely studied through response analysis to rare stimuli using electroencephalography (EEG). More recent researches have explored the brain circuitry of attention by applying neuroimaging techniques, such as functional magnetic resonance. This paper investigates for the first time the feasibility of automatic recognition of responses to rare stimuli by using functional near-infrared spectroscopy (fNIRS). fNIRS is a portable brain imaging modality that optically measures the cortical hemodynamic activation and may prove useful in monitoring localized activity changes in frontal cortex related to attention processes. In this preliminary study, Fisher Linear Discriminant (FLD) is used to discriminate between average responses to rare task-relevant stimuli and responses to task-irrelevant stimuli.

We culture embryonic rat hippocampal neurons to learn how small networks of neurons interact and code information. We design the networks by using microlithography to control surface chemistry that in turn controls the initial position of the neurons and strongly influences subsequent growth. The lithography also permits us to guide neurons preferentially to electrodes of a microelectrode array, with a resultant increase in recordability and excitability of the cultured neurons. Geometric control also allows us to begin to investigate the question as to whether the geometric pattern of a neuronal network influences the patterns of its neuroelectric activity. Various neuronal network behaviors can be demonstrated, including propagation of both action potential and synaptically coupled activity, graded activation of networks, convergence of information flow, and elementary learning phenomena. The immediate aim of the research is the creation of a reliable, repeatable, and robust tool for understanding neuronal information processing. Long term the results will assist basic and applied neuroscience including prosthetics and cell based biosensors.

The exact mechanism of information transfer between different brain regions is still not known. The theory of binding tries to explain how different aspects of perception or motor action combine in the brain to form a unitary experience. The theory presumes that there is no specific center in the brain that would gather the information from all the other brain centers, governing senses, motion, etc., and then make the decision about the action. Instead, the centers bind together when necessary, maybe through electromagnetic (EM) waves of specific frequency. Therefore, it is reasonable to assume that the information that is transferred between the brain centers is somehow coded in the electroencephalographic (EEG) signals. The aim of this study was to explore whether it is possible to extract the information on brain activity from the EEG signals during visuomotor tracking task. In order to achieve the goal, artificial neural network (ANN) was used. The ANN was used to predict the measured gripping-force from the EEG signal measurements and thus to show the correlation between EEG signals and motor activity. The ANN was first trained with raw EEG signals of all the measured electrodes as inputs and gripping-force as the output. However, the ANN could not be trained to perform the task successfully. If we presume that brain centers transmit and receive information through EM signals, as suggested by the binding theory, a simplified model of signal transmission in brain can be proposed. We propose a computational model of a human brain where the information between centers is transmitted as phase-modulation of certain carrier frequency. Demodulated signals were then used as the inputs for the ANN and the gripping-force signal was used as the output. It was possible to train the network to efficiently calculate the gripping-force signal from the phase-demodulated EEG signals.

We investigated by means of quantitative EEG (qEEG) analysis EEG traces of patients with brain trauma (with and without posttraumatic epilepsy) with respect to control group. The aim of our work was to determine if there are qEEG parameters sensible for traumatic and epileptic changes of brain tissue and how these parameters change after hyperventilation (HV), one of the routine methods for cerebral activation. On 69 patients (36 patients with posttraumatic epilepsy (PTE) and 33 without PTE) and control group of healthy patients (34 subjects), EEG registration and analysis was performed before and after HV: Fast Fourier analysis was performed on 16-s segments and amplitude mean value was calculated in ten frequency ranges in four EEG projections. HV induced significant differences in F7-C3 projection in all three groups of patients, so we analysed differences for each frequency range in F7-C3 projection for all three groups of patients. Significant differences are registered between group of healthy patients and patients with PTE in intervals of low frequencies (0-3 Hz) and high frequencies (6-11 Hz). After HV statistically significant difference is observed in all frequency ranges. Factor “epilepsy” (patients with trauma without PTE vs. patients with PTE) marks significant differences in high frequency ranges (6-11 Hz). For this factor, HV expands the group of significantly different frequency ranges towards the low frequency ranges. Consequently, the most sensitive frequency ranges for factor “epilepsy” are in high frequency range. There are no significant differences in any frequency ranges for factor “trauma” (control group of subjects vs. patients with brain trauma without PTE) before HV. After HV the significant differences appear in the range of low (0-2 Hz) and high frequencies (7-11 Hz). qEEG analysis is a diagnostic tool of potentially high selectivity in differential diagnosis of patients with brain trauma with or without PTE.