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| Diploma Thesis | PreJuSER-61597 |
2008
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
Please use a persistent id in citations: http://hdl.handle.net/2128/3091
Report No.: Juel-4269
Abstract: Bioelectronic sensors represent a major field of interest in current research. Their ability to detect and transduce biological processes like DNA-hybridization or electrical cell signals into measurable currents generates numerous applications. For example, they can be used for non-invasive monitoring of electrical cell signals in cell cultures or for the detection of very low concentrations of biological molecules. Several approaches to improve the recording properties of bioelectronic sensors have been conducted in recent works. The sensors were optimized for higher sensitivities [1], for a low noise behavior [2] and for high spatial recognition by using large sensor arrays [3]. A common feature of those sensor systems is that they work in environments, which inherently generate large amounts of noise. At the same time, the sensed signals are very weak and must be separated from disturbing effects. Signal processing offers a wide range of methods to enhance data computation. Especially since the break through of digital signal processing (DSP) methods numerous fields, e.g. telecommunications showed great benefit from this development. By using DSP the signal detection and computation can be significantly improved. It allows to filter out relevant information, to compensate measurement errors, or provides a statistical description of signals. These are properties which are especially interesting for sensor applications, where the information is often masked by large noise components. The measurement system frequently incorporates additional errors, which must be separated from the signal of interest, and statistical descriptions can be used to estimate noise processes or to detect weak signals. This makes it interesting to use signal theory approaches in conjunction with biological measurements. Especially in the detection of action potentials of electrically active cells the potential of signal theory usage was recognized. By using wavelets [4, 5] and nonlinear energy operators [6], weak action potentials of neuronal cells can be detected. Also in the domain of cell characterization and molecule detection signal theory approaches can be found. A basic approach for biological measurements is to observe the resistivity changes of sensors itself [7]. Other methods gather additional information about the measurement system by quantifying its complex impedance [8, 9]. The impedance is affected by several elements of the measured system. By developing models, these parameters can be extracted to deduce further information about the [...]
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