Adaptive multiscale biological signal processing

Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.

Quantitative and qualitative comparison of 3.0 T and 1.5 T MR imaging of the liver in patients with diffuse parenchymal liver disease

Purpose: The purpose of our study was to compare signal characteristics and image qualities of MR imaging at 3.0 T and 1.5 T in patients with diffuse parenchymal liver disease. Materials and methods: 25 consecutive patients with diffuse parenchymal liver disease underwent abdominal MR imaging at both 3.0 T and 1.5 T within a 6-month interval. A retrospective study was conducted to obtain quantitative and qualitative data from both 3.0 T and 1.5 T MRI. Quantitative image analysis was performed by measuring the signal-to-noise ratios (SNRs) and the contrast-to-noise ratios (CNRs) by the Students t-test. Qualitative image analysis was assessed by grading each sequence on a 3- and 4-point scale, regarding the presence of artifacts and image quality, respectively. Statistical analysis consisted of the Wilcoxon signed-rank test. Results: the mean SNRs and CNRs of the liver parenchyma and the portal vein were significantly higher at 3.0 T than at 1.5 T on portal and equilibrium phases of volumetric interpolated breath-hold examination (VIBE) images (P < 0.05). The mean SNRs were significantly higher at 3.0 T than at 1.5 T on T1-weighted spoiled gradient echo (SGE) images (P < 0.05). However, there were no significantly differences on T2-weighted short-inversion-time inversion recovery (STIR) images. Overall image qualities of the 1.5 T noncontrast T1- and T2-weighted sequences were significantly better than 3.0 T (P < 0.01). In contrast, overall image quality of the 3.0 T post-gadolinium VIBE sequence was significantly better than 1.5 T (P< 0.01). Conclusions: MR imaging of post-gadolinium VIBE sequence at 3.0 T has quantitative and qualitative advantages of evaluating for diffuse parenchymal liver disease. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

Exploiting sensor redundancy for the calculation of fractional derivatives in the presence of noise

In this paper a new method for the calculation of the fractional expressions in the presence of sensor redundancy and noise, is presented. An algorithm, taking advantage of the signal characteristics and the sensor redundancy, is tuned and optimized through genetic algorithms. The results demonstrate the good performance for different types of expressions and distinct levels of noise.

A sensor classification strategy for robotic manipulators

In practice the robotic manipulators present some degree of unwanted vibrations. The advent of lightweight arm manipulators, mainly in the aerospace industry, where weight is an important issue, leads to the problem of intense vibrations. On the other hand, robots interacting with the environment often generate impacts that propagate through the mechanical structure and produce also vibrations. In order to analyze these phenomena a robot signal acquisition system was developed. The manipulator motion produces vibrations, either from the structural modes or from endeffector impacts. The instrumentation system acquires signals from several sensors that capture the joint positions, mass accelerations, forces and moments, and electrical currents in the motors. Afterwards, an analysis package, running off-line, reads the data recorded by the acquisition system and extracts the signal characteristics. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Because of the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White (White, 1987) presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion phenomena, sensor materials and fields of application. Michahelles and Schiele (Michahelles & Schiele, 2003) systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. This framework not only guides application designers for choosing meaningful sensor subsets, but also can inspire new systems and leads to the evaluation of existing applications. Today���s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention (Esteban et al., 2005; Luo & Kay, 1990). A survey of the state of the art in sensor fusion for robotics can be found in (Hackett & Shah, 1990). Henderson and Shilcrat (Henderson & Shilcrat, 1984) introduced the concept of logic sensor that defines an abstract specification of the sensors to integrate in a multisensor system. The recent developments of micro electro mechanical sensors (MEMS) with unwired communication capabilities allow a sensor network with interesting capacity. This technology was applied in several applications (Arampatzis & Manesis, 2005), including robotics. Cheekiralla and Engels (Cheekiralla & Engels, 2005) propose a classification of the unwired sensor networks according to its functionalities and properties. This paper presents a development of a sensor classification scheme based on the frequency spectrum of the signals and on a statistical metrics. Bearing these ideas in mind, this paper is organized as follows. Section 2 describes briefly the robotic system enhanced with the instrumentation setup. Section 3 presents the experimental results. Finally, section 4 draws the main conclusions and points out future work.

Classifica����o e previs��o de cardiotocogramas usando algoritmos baseados em redes neuronais

Mestrado em Computa����o e Instrumenta����o M��dica

Vibration analysis for Bearing outer race condition diagnostics

This paper investigates defect detection methodologies for rolling element bearings through vibration analysis. Specifically, the utility of a new signal processing scheme combining the High Frequency Resonance Technique (HFRT) and Adaptive Line Enhancer (ALE) is investigated. The accelerometer is used to acquire data for this analysis, and experimental results have been obtained for outer race defects. Results show the potential effectiveness of the signal processing technique to determine both the severity and location of a defect. The HFRT utilizes the fact that much of the energy resulting from a defect impact manifests itself in the higher resonant frequencies of a system. Demodulation of these frequency bands through use of the envelope technique is then employed to gain further insight into the nature of the defect while further increasing the signal to noise ratio. If periodic, the defect frequency is then present in the spectra of the enveloped signal. The ALE is used to enhance the envelope spectrum by reducing the broadband noise. It provides an enhanced envelope spectrum with clear peaks at the harmonics of a characteristic defect frequency. It is implemented by using a delayed version of the signal and the signal itself to decorrelate the wideband noise. This noise is then rejected by the adaptive filter that is based upon the periodic information in the signal. Results have been obtained for outer race defects. They show the effectiveness of the methodology to determine both the severity and location of a defect. In two instances, a linear relationship between signal characteristics and defect size is indicated.

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 �� 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients.

MS Measurements

RMS measuring device is a nonlinear device consisting of linear and nonlinear devices. The performance of rms measurement is influenced by a number of factors; i) signal characteristics, 2) the measurement technique used and 3) the device characteristics. RMS measurement is not simple, particularly when the signals are complex and unknown. The problem of rms measurement on high crest-factor signals is fully discussed and a solution to this problem is presented in this thesis. The problem of rms measurement is systematically analized and found to have mainly three types of errors: (1) amplitude or waveform error 2) Frequency error and (3) averaging error. Various rms measurement techniques are studied and compared. On the basis of this study the rms -measurement is reclassified three categories: (1) Wave-form-error-free measurement (2) High-frequncy-error measurement and (3) Low-frequency error-free measurement. In modern digital sampled-data systems the signals are complex and waveform-error-free rms measurement is highly appreciated. Among the three basic blocks of rms measuring device the squarer is the most important one. A squaring technique is selected, that permits shaping of the squarer error characteristic in such a way as to achieve waveform-errob free rms measurement. The squarer is designed, fabricated and tested. A hybrid rms measurement using an analog rms computing device and digital display combines the speed of analog techniques and the resolution and ease of measurement of digital techniques. An A/D converter is modified to perform the square-rooting operation. A 10-V rms voltmeter using the developed rms detector is fabricated and tested. The chapters two, three and four analyse the problems involved in rms measurement and present a comparative study of rms computing techniques and devices. The fifth chapter gives the details of the developed rms detector that permits wave-form-error free rms measurement. The sixth chapter, enumerates the the highlights of the thesis and suggests a list of future projects

Wide dynamic range homodyne interferometry method and its application for piezoactuator displacement measurements

Funda����o de Amparo �� Pesquisa do Estado de S��o Paulo (FAPESP)

Description and repeatability of a newly developed spinal cord injury scale for dogs.

The objectives of this study were to describe a new spinal cord injury scale for dogs, evaluate repeatability through determining inter-rater variability of scores, compare these scores to another established system (a modified Frankel scale), and determine if the modified Frankel scale and the newly developed scale were useful as prognostic indicators for return to ambulation. A group of client-owned dogs with spinal cord injury were examined by 2 independent observers who applied the new Texas Spinal Cord Injury Score (TSCIS) and a modified Frankel scale that has been used previously. The newly developed scale was designed to describe gait, postural reactions and nociception in each limb. Weighted kappa statistics were utilized to determine inter-rater variability for the modified Frankel scale and individual components of the TSCIS. Comparisons were made between raters for the overall TSCIS score and between scales using Spearman's rho. An additional group of dogs with surgically treated thoracolumbar disk herniation was enrolled to look at correlation of both scores with spinal cord signal characteristics on magnetic resonance imaging (MRI) and ambulatory outcome at discharge. The actual agreement between raters for the modified Frankel scale was 88%, with a weighted kappa value of 0.93. The TSCIS had weighted kappa scores for gait, proprioceptive positioning and nociception components that ranged from 0.72 to 0.94. Correlation between raters for the overall TSCIS score was Spearman's rho=0.99 (P<0.001). Comparison of the overall TSCIS score to the modified Frankel score resulted in a Spearman's rho value of 0.90 (P<0.001). The modified Frankel score was weakly correlated with the length of hyperintensity of the spinal cord: L2 vertebral body length ratio on mid-sagittal T2-weighted MRI (Spearman's rho=-0.45, P=0.042) as was the overall TSCIS score (Spearman's rho=-0.47, P=0.037). There was also a significant difference in admitting modified Frankel scores (P=0.029) and admitting overall TSCIS scores (P=0.02) between dogs that were ambulatory at discharge and those that were not. Results from this study suggest that the TSCIS is an easy to administer scale for evaluating canine spinal cord injury based on the standard neurological exam and correlates well with a previously described modified Frankel scale.

Photonic processing subsystem based on visual cortex architecture

Optical signal processing in any living being is more complex than the one obtained in artificial systems. Cortex architecture, although only partly known, gives some useful ideas to be employed in communications. To analyze some of these structures is the objective of this paper. One of the main possibilities reported is handling signals in a parallel way. As it is shown, according to the signal characteristics each signal impinging onto a single input may be routed to a different output. At the same time, identical signals, coming to different inputs, may be routed to the same output without internal conflicts. This is due to the change of some of their characteristics in the way out when going through the intermediate levels. The simulation of this architecture is based on simple logic cells. The basis for the proposed architecture is the five layers of the mammalian retina and the first levels of the visual cortex.

Firefly ���femmes fatales��� acquire defensive steroids (lucibufagins) from their firefly���prey

Female fireflies of the genus Photuris, the so-called firefly ���femmes fatales,��� prey on male fireflies of the genus Photinus. The females are able to entrap the males by faking the flash signal characteristics of the Photinus female. We found that by feeding on Photinus males, Photuris females gain more than nutrients. They also acquire defensive steroidal pyrones called lucibufagins, which are contained in Photinus but which Photuris fireflies are unable to produce on their own. Photuris females that eat Photinus males or lucibufagin are rejected by Phidippus jumping spiders. Lucibufagin itself proved to be a deterrent to such spiders. Field-collected Photuris females contain lucibufagin in varying amounts. The more lucibufagin they contain the more unacceptable they are to Phidippus.

Disordered connectivity in the autistic brain:challenges for the 'new psychophysiology'

In 2002, we published a paper [Brock, J., Brown, C., Boucher, J., Rippon, G., 2002. The temporal binding deficit hypothesis of autism. Development and Psychopathology 142, 209-224] highlighting the parallels between the psychological model of 'central coherence' in information processing [Frith, U., 1989. Autism: Explaining the Enigma. Blackwell, Oxford] and the neuroscience model of neural integration or 'temporal binding'. We proposed that autism is associated with abnormalities of information integration that is caused by a reduction in the connectivity between specialised local neural networks in the brain and possible overconnectivity within the isolated individual neural assemblies. The current paper updates this model, providing a summary of theoretical and empirical advances in research implicating disordered connectivity in autism. This is in the context of changes in the approach to the core psychological deficits in autism, of greater emphasis on 'interactive specialisation' and the resultant stress on early and/or low-level deficits and their cascading effects on the developing brain [Johnson, M.H., Halit, H., Grice, S.J., Karmiloff-Smith, A., 2002. Neuroimaging of typical and atypical development: a perspective from multiple levels of analysis. Development and Psychopathology 14, 521-536].We also highlight recent developments in the measurement and modelling of connectivity, particularly in the emerging ability to track the temporal dynamics of the brain using electroencephalography (EEG) and magnetoencephalography (MEG) and to investigate the signal characteristics of this activity. This advance could be particularly pertinent in testing an emerging model of effective connectivity based on the balance between excitatory and inhibitory cortical activity [Rubenstein, J.L., Merzenich M.M., 2003. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes, Brain and Behavior 2, 255-267; Brown, C., Gruber, T., Rippon, G., Brock, J., Boucher, J., 2005. Gamma abnormalities during perception of illusory figures in autism. Cortex 41, 364-376]. Finally, we note that the consequence of this convergence of research developments not only enables a greater understanding of autism but also has implications for prevention and remediation. �� 2006.