Real-time data analysis of action potentials

In this paper an automated approach for the measurement of the electrical activity of a biological neural network is proposed. This method can be applied in the drug development process to verify the lead compounds of the high throughput screening with cell-based assays and there with reducing animal experiments. This verification is also called high content screening. To be able to detect and to evaluate action potentials, which mainly represent the electrical cell activity, neurons are cultured on a silicon sensor chip with integrated electronics and a multielectrode array (MEA). Due to the high parallelism of the measurement efficient and flexible algorithms are needed to assess and to classify the acquired data in real time. A system, consisting of a field programmable gate array (FPGA) and a digital signal processor (DSP) provide the required implementation platform. Filtering based on the discrete wavelet transform removes superimposed noise and low frequency disturbances from the neural signal. This analysis offers also a method to compute an adaptive threshold, which is essential for the detection process. Subsequently the measured data is classified to provide the user with a feedback of the experiment. First promising evaluation results from simulations and proof of concept hardware implementations can be presented.