Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.

Abstract-Many models of spiking neural networks heavily rely on exponential waveforms. On neuromorphic multiprocessor systems like SpiNNaker, they have to be approximated by dedicated algorithms, often dominating the processing load. Here we present a processor extension for fast calculation of exponentials, aimed at integration in the next-generation SpiNNaker system. Our implementation achieves single-LSB precision in a 32bit fixed-point format and 250Mexp/s throughput at 0.44nJ/exp for nominal supply (1.0V), or 0.21nJ/exp at 0.7V supply and 77Mexp/s, demonstrating a throughput multiplication of almost 50 and 98% energy reduction at 2% area overhead per processor on a 28nm CMOS chip.

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