The thesis is composed of two topics : a low-noise instru-mentation amplifier (IA) for neural signal sensing and a low-power implantable bladder pressure monitor SOC (system-on-chip). A low-noise instrumentation amplifier for bio-medical appli-cations is proposed in the first topic. It is designed for sampling vague neural signals thanks to its high gain, high CMRR in a pre-defined bandwidth. A low-power implantable bladder pressure monitor system is presented in the next topic. The system contains several parts : a commercial pressure sensor, an IA, an analog to digital converter (ADC), a parallel to serial converter (PtoS), an RF transmitter and a sleep controller. The IA with 1-atm canceling is designed for high resolution and linearity in the pre-defined bladder pressure range. For low power and low speed applications, a successive approximation ADC (SA ADC) is employed in the system. A clear flag is added to the PtoS to enhance reliability. Our chip saves a great portion of power to extend the processing time owing to the novel sleep controller.

In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

The Vagus nerve has shown to play an important role regarding inflammatory diseases, regulating the production of proteins that mediate inflammation. Two important such proteins are the pro-inflammatory cytokines, TNF and IL-1β. This thesis makes use of Vagus nerve recordings, where TNF and IL-1β are subsequently injected in mice, with the aim to see if cytokine-specific information can be extracted. To this end, a type of semi-supervised learning approach is applied, where the observed waveform-data are modeled using a conditional probability distribution. The conditioning is done based on an estimate of how often each observed waveform occurs and local maxima of the conditional distribution are interpreted as candidate-waveforms to encode cytokine information. The methodology yields varying, but promising results. The occurrence of several candidate waveforms are found to increase substantially after exposure to cytokine. Difficulties obtaining coherent results are discussed, as well as different approaches for future work. / Vagusnerven har visat sig spela en viktig roll beträffande inflammatoriska sjukdomar. Denna nerv reglerar produktionen av inflammatoriska protein, som de inflammationsfrämjande cytokinerna TNF och IL-1β. Detta arbete använder sig av elektroniska mätningar av Vagusnerven i möss som under tiden blir injicerade med de två cytokinerna TNF och IL-1β. Syftet med arbetet är att undersöka om det är möjligt att extrahera information om de specifika cytokinerna från Vagusnervmätningarna. För att uppnå detta designar vi en semi-vägledd lärandemetod som modellerar dem observerade vågformerna med en betingad sannolikhetsfunktion. Betingandet baseras på en uppskattning av hur ofta varje enskild vågform förekommer och lokala maximum av den betingade sannolikhetsfunktionen tolkas som möjliga kandidat-vågformer att innehålla cytokin-information. Metodiken ger varierande, men lovande resultat. Förekomsten av flertalet kandidat-vågformer har en tydlig ökning efter tidpunkten för cytokin-injektion. Vidare så diskuteras svårigheter i att uppnå konsekventa resultat för alla mätningar, samt olika möjligheter för framtida arbete inom området.

El principal motivo por el cual los sistemas de comunicación en el entrono corporal se desean con el objetivo de poder obtener y procesar señales biométricas para monitorizar e incluso tratar una condición médica sea ésta causada por una enfermedad o el rendimiento de un atleta. Dado que la base de estos sistemas está en la sensorización y el procesado, los algoritmos de procesado de señal son una parte fundamental de los mismos. Esta tesis se centra en los algoritmos de tratamiento de señales en tiempo real que se utilizan tanto para monitorizar los parámetros como para obtener la información que resulta relevante de las señales obtenidas. En la primera parte se introduce los tipos de señales y sensores en los sistemas en el entrono corporal. A continuación se desarrollan dos aplicaciones concretas de los sistemas en el entorno corporal así como los algoritmos que en las mismas se utilizan. La primera aplicación es el control de glucosa en sangre en pacientes con diabetes. En esta parte se desarrolla un método de detección mediante clasificación de patronones de medidas erróneas obtenidas con el monitor contínuo comercial "Minimed CGMS". La segunda aplicacióin consiste en la monitorizacióni de señales neuronales. Descubrimientos recientes en este campo han demostrado enormes posibilidades terapéuticas (por ejemplo, pacientes con parálisis total que son capaces de comunicarse con el entrono gracias a la monitorizacióin e interpretación de señales provenientes de sus neuronas) y también de entretenimiento. En este trabajo, se han desarrollado algoritmos de detección, clasificación y compresión de impulsos neuronales y dichos algoritmos han sido evaluados junto con técnicas de transmisión inalámbricas que posibiliten una monitorización sin cables. Por último, se dedica un capítulo a la transmisión inalámbrica de señales en los sistemas en el entorno corporal. En esta parte se estudia las condiciones del canal que presenta el entorno corporal para la transmisión de s / Traver Sebastiá, L. (2012). Real-time signal detection and classification algorithms for body-centered systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16188

Orientador: Prof. Dr. Ricardo Suyama / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, Santo André, 2018. / Uma interface cerebro-computador (BCI) e projetada para que se consiga, de modo efetivo, fornecer uma via alternativa de comunicacao entre o cerebro do usuario e o computador. Sinais captados por meio de eletrodos, tipicamente posicionados no escalpo do individuo, sao previamente processados para que haja eliminacao de ruidos externos. A partir dai, diversas tecnicas para processamento de sinais sao utilizadas para posteriormente classificar os sinais registrados e realizar a traducao do estado mental do usuario em um comando especifico a ser executado pelo computador. No presente trabalho sao utilizadas tecnicas de representacao esparsa dos sinais para a extracao de caracteristicas relevantes para classificacao dos mesmos, com intuito de aumentar a robustez e melhorar o desempenho do sistema. Para a extracao de sinais esparsos, foram utilizados algoritmos de criacao de dicionarios, a partir dos quais e possivel obter uma representacao esparsa para todo o subespaco de sinal. No trabalho foram utilizados 5 diferentes algoritmos de criacao de dicionario: Metodo de direcoes otimas (MOD), K-SVD, RLS-DLA, LS-DLA e Aprendizado de dicionario Online (ODL). A classificacao dos sinais foi realizada com o metodo de .. vizinhos mais proximos (k - NN). Os resultados obtidos com a abordagem de representacao esparsa foram comparados com os resultados do BCI Competition IV dataset 2a. Para o primeiro colocado da competicao foi obtido, em termos do coeficiente kappa, uma acuracia de 0.57 enquanto que no trabalho utilizando os metodos esparsos, obteve-se, em coeficiente kappa, uma acuracia de 0.90. Em comparacao obteve-se um ganho de 0.33 de acuracia, onde se deduz que o uso de sinais esparsos pode ser benefico para o dificil problema de se projetar uma interface cerebro computador. / A brain computer interface (BCI) is designed to effectively translate commands thought by human individuals into commands that a computer can effectively understand. Electrical impulses generated from the brain sculp are recorded from a device called an electroencephalograph and are preprocessed for elimination of external noise. From there, several techniques for signal processing are used to later classify the signals obtained by the electroencephalograph. In this work, techniques for sparse representation of signals are used for feature extraction, in order to increase robustness and system performance. For the extraction of sparse signals, five different dictionary learning algorithms were used, being able to produce a basis capable of represensing the entire signal subspace. In this work, 5 different dictionary learning algorithms were used: Method of Optimal Directions (MOD), K-SVD, Recursive Least Square Dictionary Learning (RLS-DLA), Least Square Dictionary Learning (LS-DLA) and Online Dictionary Learning (ODL). For the classification task, the k-NN method was used. The simulation results obtained with this approach were compared with the best BCI Competition IV dataset 2a results. For the first place in the competition, an accuracy of 0.57 was obtained, in terms of the kappa coefficient, whereas in the work using the sparse methods, a kappa coefficient of 0.90 was obtainned, improving accuracy in 0.33 accuracy was obtained, which indicates that the use of sparse signals may be beneficial to the difficult problem of designing a brain computer interface.

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. These signals also have transient structures related to spiking or sudden onset of a stimulus, which have a duration not exceeding tens of milliseconds. Further, brain signals are highly non-stationary because both behavioral state and external stimuli can change over a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal. In Chapter 2, we describe a multi-scale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both sharp stimulus-onset transient and sustained gamma rhythm in local field potential recorded from the primary visual cortex. Gamma rhythm (30 to 80 Hz), often associated with high-level cortical functions, has been proposed to provide a temporal reference frame (“clock”) for spiking activity, for which it should have least center frequency variation and consistent phase for extended durations. However, recent studies have proposed that gamma occurs in short bursts and it cannot act as a reference. In Chapter 3, we propose another gamma duration estimator based on matching pursuit (MP) algorithm, which is tested with synthetic brain signals and found to be estimating the gamma duration efficiently. Applying this algorithm to real data from awake monkeys, we show that the median gamma duration is more than 330 ms, which could be long enough to support some cortical computations.