Marta Borowska

Entropy-Based Algorithms in the Analysis of Biomedical Signals

Abstract Biomedical signals are frequently noisy and incomplete. They produce complex and high-dimensional data sets. In these mentioned cases, the results of traditional methods of signal processing can be skewed by noise or interference present in the signal. Information entropy, as a measure of disorder or uncertainty in the data, was introduced by Shannon. To date, many different types of entropy methods have appeared with many different application areas. The purpose of this paper is to present a short overview of some methods of entropy analysis and to discuss their suitability for use in the analysis of biomedical signals.

Fractal texture analysis of the healing process after bone loss

Radiological assessment of treatment effectiveness of guided bone regeneration (GBR) method in postresectal and postcystal bone loss cases, observed for one year. Group of 25 patients (17 females and 8 males) who underwent root resection with cystectomy were evaluated. The following combination therapy of intraosseous deficits was used, consisting of bone augmentation with xenogenic material together with covering regenerative membranes and tight wound closure. The bone regeneration process was estimated, comparing the images taken on the day of the surgery and 12 months later, by means of Kodak RVG 6100 digital radiography set. The interpretation of the radiovisiographic image depends on the evaluation ability of the eye looking at it, which leaves a large margin of uncertainty. So, several texture analysis techniques were developed and used sequentially on the radiographic image. For each method, the results were the mean from the 25 images. These methods compute the fractal dimension (D), each one having its own theoretic basis. We used five techniques for calculating fractal dimension: power spectral density method, triangular prism surface area method, blanket method, intensity difference scaling method and variogram analysis. Our study showed a decrease of fractal dimension during the healing process after bone loss. We also found evidence that various methods of calculating fractal dimension give different results. During the healing process after bone loss, the surfaces of radiographic images became smooth. The result obtained show that our findings may be of great importance for diagnostic purpose.

Selection of Phase Space Reconstruction Parameters for EMG Signals of the Uterus

Abstract Biological time series have a finite number of samples with noise included in them. Because of this fact, it is not possible to reconstruct phase space in an ideal manner. One kind of biomedical signals are electrohisterographical (EHG) datasets, which represent uterine muscle contractile activity. In the process of phase space reconstruction, the most important thing is suitable choice of the method for calculating the time delay τ and embedding dimension d, which will reliably reconstruct the original signal. The parameters used in digital signal processing are key to arranging adequate parameters of the analysed attractor embedded in the phase space. The aim of this paper is to present a method employed for phase space reconstruction for EHG signals that will make it possible for their further analysis to be carried out.

Fractal Texture Analysis in the Irregular Region of Interest of the Healing Process Using Guided Bone Regeneration

A fractal texture analysis technique was introduced for assessment of the healing process using Guided Bone Regeneration (GBR) after bone loss in resected and cystic areas. Fractal dimension may be used for the characterization of surface topography of medical images. In this paper we attempted to analyse fractal surface in the irregular regions of interest (irregular ROI-s) using two methods: Chen’s method and variogram. In our study a significant change for the values of fractal dimension was found.

Statistical Analysis of Radiographic Textures Illustrating Healing Process After the Guided Bone Regeneration Surgery

Radiographic images of post-gastrectomy and after the cyst removal in dental bone loss registered within 1 year period. Images of 28 patients (8 images directly after the procedure and 20 images after 12 months) who underwent the Guided Bone Regeneration surgery. The X-rays were obtained using Kodak RVG 6100 set with a resolution higher than 14 pl/mm. Textural analysis of these areas included: cropping rectangular ROIs (Regions of Interest) from both types of images (right after and ‘1 year after’ the procedure), computing following features: 3 basic (minimum, maximum, average value of pixels), GLRL matrix and GLCM. The final step was the assessment of the features and analysis of those which are statistically significant.

Transfer Entropy in Quantifying the Interactions in Preterm Labor

Evidence of the relationships between physiological data has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the preterm birth and direction of propagating uterine contraction. In this paper we study transfer entropy (TE) that is widely used to quantify interactions between biomedical time series. We are searching for indices that could detect preterm labor using 112 contractions extracted from differentiated electrohysterographical (EHG) signals. Transfer entropy was considered as a bivariate approach to quantify the bidirectional information flow from channel 1 to channel 4. TE values for women delivering after 7 days from presenting of threatened preterm labor symptoms were significantly higher than those delivering within 7 days (p < 0.01). The parameters used in this study help to estimate the potential of premature labor as it progresses. Therefore, they may be useful as early risk markers of preterm birth.

Multiresolution Analysis of EEG Signals

Abstract This paper reports on a multiresolution analysis of EEG signals. The dominant frequency components of signals with and without observed epileptic discharges were compared. The study showed that there were significant differences in dominant frequency between the signals with epileptic discharges and the signals without discharges. This gives the ability to identify epilepsy during EEG examination. The frequency of the signals coming from the frontal, central, parietal and occipital channels are similar. Multiresolution analysis can be used to describe the activity of brain waves and to try to predict epileptic seizures, thereby contributing to precise medical diagnoses.

Walsh-Hadamard spectral analysis of signals representing bioelectrical activity of the reproductive tract in pigs

The aim of this paper is to present the use of the Walsh-Hadamard transform in the analysis of electromygraphic signals representing the uterine contractile activity in pigs. The Fourier spectral analysis is widely used in many biomedical applications. However, for binary time series the Walsh-Hadamard transform based on square or rectangular waves with peaks of ±1 is more accurate. Dominant normalized sequency can serve as a parameter describing the biomedical signal, which may have diagnostic importance.

The Hilbert Transform in Analysis of Uterine Contraction Activity

Abstract Prevention and early diagnosis of forthcoming preterm labor is of vital importance in preventing child mortality. To date, our understanding of the coordination of uterine contractions is incomplete. Among the many methods of recording uterine contractility, electrohysterography (EHG) – the recording of changes in electrical potential associated with contraction of the uterine muscle, seems to be the most important from a diagnostic point of view. There is some controversy regarding whether EHG may identify patients with a high risk of preterm delivery. There is a need to check various digital signal processing techniques to describe the recorded signals. The study of synchronization of multivariate signals is important from both a theoretical and a practical point of view. Application of the Hilbert transformation seems very promising.