Informações sobre as características funcionais e estruturais da unidade motora (UM) são altamente relevantes em investigações fisiológicas e nos estudos clínicos das disfunções neuromusculares. A eletromiografia (EMG) é um método adequado para obtenção dessas informações. Entretanto, devido à dificuldade na separação da atividade individual de uma unidade motora das outras que estão simultaneamente ativas, seu uso em clínica prática se dá comumente através de métodos invasivos, empregando eletrodos de agulha ou fios implantados. Apesar da EMG de superfície ser não-invasiva e, portanto mais apropriada para aplicações clínicas, não é usada em clínica porque não há até o presente um método satisfatório para decomposição do sinal EMG de superfície. Um EMG de superfície é muito mais difícil de decompor devido a significante superposição dos Potenciais de Ação das UMs (MUAPs) e a relação sinal-ruído relativamente baixa, se comparada aos métodos invasivos. Defendemos que a separação da atividade individual das UMs pode ser feita de modo não-invasivo aliando-se técnicas de aquisição altamente especializadas com técnicas usadas em reconhecimento de padrões. Desenvolvemos um método para decomposição de EMGs de superfície, a partir do qual foi possível extrair características relevantes das UMs, que permitem seu uso em avaliação e diagnóstico de desordens neuromusculares. Em nossa abordagem, o sinal EMG é inicialmente captado sob contração isométrica fraca usando eletrodos desuperfície. O sinal EMG bruto passa em seguida por um filtro Diferencial Passa-Baixas Ponderado (DPBP) em série com um detector de picos, que detecta os picos de MUAPs e extrai suas formas de onda. Na sequência, o conjunto de MUAPs extraído é classificado por uma rede neural SOM, e os MUAPs agrupados pela similaridade de suas formas de onda. No próximo passo a informação temporal dos disparos é checada, eliminando possíveis erros de classificação, e finalmente os Trens de MUAPs (MUAPTs) das UMs individuais são reconstituídos do EMG original. As estatísticas de disparos (IPI) bem como as formas de ondas dos MUAPs das respectivas UMs são então extraídas e armazenadas para estudos posteriores. Resultados preliminares obtidos com EMGs normais e patológicos, extraídos de membros superiores sob contração fraca, indicam que, o método mostrou-se apto a decompor EMGs de superfícies, além de potencial para aplicações em estudos clínicos não-invasivos de disfunções neuromusculares.Informações sobre as características funcionais e estruturais da unidade motora (UM) são altamente relevantes em investigações fisiológicas e nos estudos clínicos das disfunções neuromusculares. A eletromiografia (EMG) é um método adequado para obtenção dessas informações. Entretanto, devido à dificuldade na separação da atividade individual de uma unidade motora das outras que estão simultaneamente ativas, seu uso em clínica prática se dá comumente através de métodos invasivos, empregando eletrodos de agulha ou fios implantados. Apesar da EMG de superfície ser não-invasiva e, portanto mais apropriada para aplicações clínicas, não é usada em clínica porque não há até o presente um método satisfatório para decomposição do sinal EMG de superfície. Um EMG de superfície é muito mais difícil de decompor devido a significante superposição dos Potenciais de Ação das UMs (MUAPs) e a relação sinal-ruído relativamente baixa, se comparada aos métodos invasivos. Defendemos que a separação da atividade individual das UMs pode ser feita de modo não-invasivo aliando-se técnicas de aquisição altamente especializadas com técnicas usadas em reconhecimento de padrões. Desenvolvemos um método para decomposição de EMGs de superfície, a partir do qual foi possível extrair características relevantes das UMs, que permitem seu uso em avaliação e diagnóstico de desordens neuromusculares. Em nossa abordagem, o sinal EMG é inicialmente captado sob contração isométrica fraca usando eletrodos desuperfície. O sinal EMG bruto passa em seguida por um filtro Diferencial Passa-Baixas Ponderado (DPBP) em série com um detector de picos, que detecta os picos de MUAPs e extrai suas formas de onda. Na sequência, o conjunto de MUAPs extraído é classificado por uma rede neural SOM, e os MUAPs agrupados pela similaridade de suas formas de onda. No próximo passo a informação temporal dos disparos é checada, eliminando possíveis erros de classificação, e finalmente os Trens de MUAPs (MUAPTs) das UMs individuais são reconstituídos do EMG original. As estatísticas de disparos (IPI) bem como as formas de ondas dos MUAPs das respectivas UMs são então extraídas e armazenadas para estudos posteriores. Resultados preliminares obtidos com EMGs normais e patológicos, extraídos de membros superiores sob contração fraca, indicam que, o método mostrou-se apto a decompor EMGs de superfícies, além de potencial para aplicações em estudos clínicos não-invasivos de disfunções neuromusculares. / Information on the functional and structural characteristics of the motor unit (MU) they are highly important in physiologic investigations and in the clinical studies of the neuromuscular dysfunctions. The electromyography (EMG) it is an appropriate method for obtaining of that information. However, due to the difficulty in the separation of the individual activity of a motor unit of the another that are simultaneously active, your use in practical clinic happen commonly through methods invasive, employing needle electrodes or implanted threads. In spite of surface EMG to be non-invasive and, therefore more appropriate for clinical applications, it is not used at clinic because there is not until the present a satisfactory method for decomposition of the surface EMG sign. A surface EMG is much more difficult of decomposing due to significant overlap of the Motor Unit Action Potentials (MUAPs) and the relationship sign-noise relatively low, if compared to the invasive methods. We defended that the separation of the individual activity of MUs can be made in way non-invasive allying highly specialized acquisition techniques with techniques used in recognition of patterns. We developed a method for decomposition of surface EMGs, starting from which was possible to extract important characteristics of MUs, which allow your use in evaluation and diagnosis of neuromuscular disorders. In our approach, the sign EMG is captured initially under weak isometriccontraction using surface electrodes. The sign EMG raw raisin soon after for a Biased Low-Pass Differential filter (BLPD) in series with a detector of peaks, that detects the peaks of MUAPs and it extracts your wave forms. In the sequence, a SOM neural network classifies the set of extracted MUAPs, and MUAPs are clustered by the similarity in your wave shape. In the next step the temporal information of the discharges is checked, eliminating possible classification mistakes, and finally the MUAPs Trains (MUAPTs) of individual MUs they are reconstituted of original EMG. The statistics of discharges (IPI) as well as the forms of waves of MUAPs of respective MUs are then extracted and stored for subsequent studies. Results preliminaries obtained with normal and pathological EMGs, extracted of superior members under weak contraction, they indicate that, the method was shown capable to decompose surfaces EMGs, besides potential for applications in clinical studies non-invasive of neuromuscular dysfunctions.

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Dans cette thèse, le comportement d'un modèle mathématique permettant de transcrire la dynamique neuronale est étudié : le système de FitzHugh-Nagumo. En particulier, nous nous intéressons au caractère aléatoire d'ouverture et de fermeture des canaux ioniques d'un neurone qui reçoit ou non un stimulus. Ce caractère aléatoire de la dynamique neuronale est considéré, dans notre modèle, comme un bruit. Dans un premier temps, le comportement du modèle de FitzHugh-Nagumo a été caractérisé au voisinage de la bifurcation d'Andronov-Hopf qui traduit la transition entre l'état d'activation et l'état de repos du neurone. Classiquement, un neurone positionné à l'état de repos ne produit aucun potentiel d'action. Cependant, il a été montré un phénomène pour lequel une quantité appropriée de bruit permet la production de potentiels d'action des plus réguliers : la résonance cohérente. Le deuxième effet observé lors de simulations numériques permet au neurone d'améliorer la détection et l'encodage d'un signal subliminal : il s'agit de la résonance stochastique. De plus, cette thèse s'inscrit dans un contexte électronique puisqu'en plus de simuler numériquement le système de FitzHugh-Nagumo, les résultats de simulations ont également été confirmés en réalisant un circuit électronique. En effet, nous avons reproduit la dynamique non linéaire du système de FitzHugh-Nagumo à l'aide de ce circuit électronique. Cela a permis de mettre en évidence expérimentalement les deux phénomènes de résonance cohérente et de résonance stochastique pour lesquelles le bruit peut avoir une influence constructive sur le comportement de notre circuit électronique. / We study the nonlinear FitzHugh-Nagumo model witch describes the dynamics of excitable neural element. It is well known that this system exhibits three different possible responses. Indeed, the system can be mono-stable, oscillatory or bistable. In the oscillatory regime, the system periodically responds by generating action potential. By contrast, in the mono-stable state the system response remains constant after a transient. Under certain conditions, the system can undergo a bifurcation between the stable and the oscillatory regime via the so called Andronov-Hopf bifurcation. In this Phd thesis, we consider the FitzHugh-Nagumo model in the stable state, that is set near the Andronov-Hopf bifurcation. Moreover, we take into account the contribution of noise witch can induces two phenomena coherence resonance and stochastic resonance. First, without external driving, we show the effect of coherence resonance since a critical noise level enhances the regularity of the system response. Another numerical investigation reports how noise can allow to detect a subthreshold deterministic signal applied to the system. In this case, an appropriate amount of noise maximizes the signal to noise ratio reveling the stochastic resonance signature. Besides this numerical studies, we have also built a non linear circuit simulating the FitzHugh-Nagumo model under the presence of noise. This circuit has allowed to confirm experimentally the numerical observation of stochastic resonance and coherence resonance. Therefor, this electronic circuit contributes a framework for further experimental investigation in the field of neural sciences to better understand the role of noise in neural encoding.

This thesis deals with the problem with cluster analysis and biosignal classification options. The principle of cluster analysis, methods for calculating distances between objects and the standard process in the implementation of clustering are described in the first part. For biosignals processing,it is necessary to get familiar with the primary parameters of these signals in the following sections of thesis, process biosignals and methods for recording of action potentials described. Based on studying different clustering methods is presented a program with the applied method kmedoid in the next section of this thesis. The steps of this program are described in detail and in the end of thesis functionality is tested on a database of signals ÚBMI.

Diploma thesis deals with the basic description of cardiac muscle cells, the mechanism of its contraction and events associated with contractions. There are different types of methods which can be used for measuring of contractions and for evaluation of cell length. This work describe these methods and evaluate their pros and cons. Based on available information and technical possibilities is one of the methods chosen and used for the design of block diagram system for measuring of contraction of isolated heart cells in real time. The practical part of this diploma thesis deals with the designing of a system which allows processing the image of isolated cardiac muscle cells that facilitate the detection of the cells edge. For this purpose it was created a device that allows the user to select a single row from television signal containing the image information from a location where is the currently selected row. Thus obtained image information can be used for cells edge detection and measuring of its length and contractions.

Dissertation is focused on the application of optical measurement methods using techniques of optical microscopy and fluorescence microscopy in measurements of electromechanical characteristics of isolated cardiac cells and clusters of differentiated cardiomyocytes. The first proposed method uses a practical combination of fluorescence microscopy equipped with fluorescent fast and high-resolution camera and atomic force microscopy for simultaneous measurement of calcium transients and contraction of cardiomyocyte clusters. The signals obtained undergoes filtration, processing and analysis. Result function parameters obtained by analyzing signals after application of caffeine are evaluated by comparison with functional parameters obtained during the control measurement. The second proposed method is applied to the cardiomyocyte clusters for the purpose of cardiomyocyte contraction signals measurement. The signals obtained by optical methods are analyzed and compared with the reference signal obtained using atomic force microscopy. Optical measurement method of cell contractins based on detection of cell ends using adjusting of microscopy images by re-sharpening and fluorescence method for cardiomyocyte contractions measurements were designed to increase realiability in simultaneous measurement of cell contractions simultaneously with calcium transients in isolated cardiomyocytes experiments.

<div>Electrical nerve stimulation (ENS) is an emerging therapy for many neurological disorders. Compared with conventional one-way stimulations, closed-loop ENS approaches increase the stimulation efficacy and minimize patient's discomfort by constantly adjusting the stimulation parameters according to the feedback biomarkers from patients. Wireless neurostimulation devices capable of both stimulation and telemetry of recorded physiological signals are welcome for closed-loop ENS systems to improve the quality and reduce the costs of treatments, and real-time digital signal processing (DSP) engines processing and extracting features from recorded signals can reduce the data transmission rate and the resulting power consumption of wireless devices. Electrically-evoked compound action potential (ECAP) is an objective measure of nerve activity and has been used as the feedback biomarker in closed-loop ENS systems including neural response telemetry (NRT) systems and a newly proposed autonomous nerve control (ANC) platform. It's desirable to design a DSP engine for real-time processing of ECAP in closed-loop ENS systems. </div><div><br></div><div>This thesis focuses on developing the DSP architecture for real-time processing of ECAP, including stimulus artifact rejection (SAR), denoising, and extraction of nerve fiber responses as biomedical features, and its VLSI implementation for optimal hardware costs. The first part presents the DSP architecture for real-time SAR and denoising of ECAP in NRT systems. A bidirectional-filtered coherent averaging (BFCA) method is proposed, which enables the configurable linear-phase filter to be realized hardware efficiently for distortion-free filtering of ECAPs and can be easily combined with the alternating-polarity (AP) stimulation method for SAR. Design techniques including folded-IIR filter and division-free averaging are incorporated to reduce the computation cost. The second part presents the fiber-response extraction engine (FREE), a dedicated DSP engine for nerve activation control in the ANC platform. FREE employs the DSP architecture of the BFCA method combined with the AP stimulation, and the architecture of computationally efficient peak detection and classification algorithms for fiber response extraction from ECAP. FREE is mapped onto a custom-made and battery-powered wearable wireless device incorporating a low-power FPGA, a Bluetooth transceiver, a stimulation and recording analog front-end and a power-management unit. In comparison with previous software-based signal processing, FREE not only reduces the data rate of wireless devices but also improves the precision of fiber response classification in noisy environments, which contributes to the construction of high-accuracy nerve activation profile in the ANC platform. An application-specific integrated circuit (ASIC) version of FREE is implemented in 180-nm CMOS technology, with total chip area and core power consumption of 19.98 mm² and 1.95 mW, respectively. </div><div><br></div>