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Cardiovascular dysregulation and autonomic neuropathy are common complications of diabetes mellitus (DM). Although autonomic neuropathy is considered as one of the late complications of DM, there are some sensitive methods, that can detect autonomic nervous system dysregulation even in early phases of DM. There is ongoing effort to apply methods based on nonlinear dynamics to improve the description and classification of different cardiac states. The aim of this study was to find out which of the heart rate variability parameters of symbolic dynamics are different in young patients with DM compared to control group. Several parameters based on 4 symbols encoding were used for quantification of heart rate variability and complexity. Our results suggest slightly reduced complexity (expressed by marginally nonsignificantly reduced number of “forbidden words”) even in young diabetic patients pointing out to another aspect of heart rate dysregulation in this group. In addition we have found qualitative difference in distribution of symbolic words expressed by parameter “wpsum02”.

There is an ongoing effort to apply methods based on nonlinear dynamics to improve the description and classification of different states and diseases. Relatively few studies were focused on autonomic neuropathy in young adults with diabetes mellitus (DM) type 1. The aim of this study was to find out which of the heart rate variability parameters derived from recurrence plot (recurrence quantification analysis parameters) are different in young patients with DM compared to control group. We have quantified various recurrence plot measures. From RQA measures based on diagonal lines in recurrence plots, we have found higher percentage of recurrence and of determinism and increased maximal length of diagonal line in DM group. Parameter Trapping Time was higher in DM group compared to control subjects. These results suggests reduced complexity and increased predictability of heart rate dynamics even in young patients with DM. RQA parameters should be used together with other HRV parameters for better description of heart rate dysregulation in various patients groups.

Insight in complex heart rate and blood pressure interactions reveals the most important aspects of autonomic control. Our main interest was in baroreceptor reflex (BRR), the most important autonomic cardiovascular reflex. We evaluated the joint symbolic dynamics of heart rate and blood pressure variations in assessing the BRR by opening the BRR loop at different levels using pharmacological blockade of β- adrenergic, α-adrenergic and M-cholinergic receptors. Experiments were done in conscious telemetred Wistar out bred male rats. The observed changes between experimental groups are promising for use of symbolic dynamic method in assessment of impaired autonomic control of the cardiovascular system.

The cardiotocography (CTG) has been introduced in in the early ‘70ies as a clinical test for checking fetal well-being during pregnancy and at the moment of delivery. The traditional approach was based on the detection of several time domain parameters of Fetal Heart Rate (FHR) signal starting from the identification of a signal baseline. With the certainty that FHR really contains important indications about potentially dangerous fetal conditions, a prototype system has been setup based on new algorithms and indices which can enhance the differences among normal and pathological fetal conditions. The basic characteristics of this system are: FHR sampled and recorded at 2 Hz; on-line traditional analysis by incremental Mantel algorithm; extraction of accelerations, decelerations, FHR variability and related parameters; extraction of power spectral components related to different physiological control mechanisms; computation of FHR signal regularity indices through the Approximate Entropy algorithm.

Approximate entropy is a measure of regularity which finds application in many problems in biomedical engineering. One drawback of the method is its high complexity which results in large execution times. The purpose of this paper is to alleviate this problem by examining three algorithms, two of which have never been suggested before for approximate entropy computation. In our experiments heart rate signals were analyzed using the three algorithms. The speedup achieved was significant.

Cardiotocography is the most commonly used noninvasive diagnostic technique that provides physicians information about fetal development (in particular about development of autonomous nervous system - ANS) and wellbeing. It allows the simultaneous recording of Fetal Heart Rate (FHR), by means of a Doppler probe, and Uterine Contractions (UC), by means of an indirect pressure transducer. Currently, in cardiotocographic devices, Doppler methodology involves autocorrelation techniques to recognize heart beats, so evaluation of inter-beats time-interval is very improved. However, recorded FHR signals may contain artifacts, because of the possible degradation, or even less, of the Doppler signal due to relative motion between probe and fetal heart, maternal movements, muscle contractions and other causes. Moreover, fetal cardiac arrhythmias can have an effect on FHR signals. These arrhythmias do not represent an expression of the physiological behavior of the ANS. Both, artifacts and cardiac arrhythmias represent outliers of the FHR signals, so they affect both time domain and time frequency signal analysis.

The cancerous attaches, mainly the lung cancers, make a serious medical problem all over the world. The early diagnoses based on chest radiographs could notably lower its mortality. The efficiency of the computers gives the possibility to facilitate the work of the radiologists by a CADsystem. But first the region of the interest, i.e. the lung fields should be determined. The lung segmentation in our sense differs from the trends accounted in literature (where the area hidden by the heart is ignored), because the left border of the left lung is located beneath the heart. In this paper we describe a method based mainly on heuristics and rules which can be used to find the contours of the lung. The algorithm is divided to five main steps: (1) finding some parameters of the lungs without long processing, (2) determining the usual lung contours, (3) finding the mediastinum, (4) finding the lower border of the left lung and (5) applying a model to achieve better results, to make some refinement.

A tracking algorithm is proposed to measure the velocity of red blood cells traveling through microvessels of tumors growing in skin flaps implanted on mice. The tracking is based on a keyhole model that describes the probable movement of a segmented cell between contiguous frames in a video sequence. When a history of movements exists, past, present and a predicted landing position define two regions of probability with a keyhole shape. This keyhole is used to determine if cells in contiguous frames should be linked to form tracks. Preprocessing segments cells from background and post-processing joins tracks and discards links that could have been formed due to noise or uncertainty. The algorithm presents several advantages over traditional methods such as kymographs or particle image velocimetry: manual intervention is restricted to the thresholding, several vessels can be analyzed simultaneously, algorithm is robust to noise and a wealth of statistical measures can be obtained. Two tumors with different geometries were analyzed; average velocities were 211±136 [μm/s] (mean±std) with a range 15.9-797 [μm/s], and 89±62 [μm/s] with a range 5.5-300 [μm/s] respectively, which are consistent with previous results in the literature.

Osteoarthritis (OA) of the knee can be described as the degradation and loss of articular cartilage. Adequate visualization of cartilage is paramount in allowing accurate and clinically meaningful assessment of cartilage surface morphology and thickness. In this paper we present a web-based user interface that allows the visualization of quantitative results (i.e., cartilage thickness) derived from MR knee images. The use of web-based technology has allowed greater access to the interface and clinically useful interactive functions for the viewing of data (i.e., cartilage thickness WearMap and MR images).

Nailfold capillaroscopy is a non invasive, simple examination, which provides exact information on the assessment of the microcirculation. The peripheral blood circulation is very sensible for certain illnesses e.g.: autoimmune diseases, diabetes. In many cases there exists a pattern specific for certain illnesses, therefore this test is capable of differentiate between them. Our aim is to develop a computer aided evaluation system for capillary microscopic images. The fist step of the evaluation is the detection of capillaries, which is done by edge detection. Classical edge detectors resulted in unsatisfactory output, therefore we tried a different approach, which is able to take into account not only the local properties of the image, but also the relations of pixels. During the test of the algorithm we obtained that with a suitable post processing procedure the capillaries shown in the picture can be detected robustfully, hence our procedure is applicable as the first step of the evaluation of these images.