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Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection.

, , , and . CoRR, (2017)

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Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection., , , and . CoRR, (2017)FPGA-Based Reduction Techniques for Efficient Deep Neural Network Deployment., and . FCCM, page 200. IEEE Computer Society, (2016)Deep Belief Networks Used on High Resolution Multichannel Electroencephalography Data for Seizure Detection., , , and . AAAI Spring Symposia, AAAI Press, (2014)An efficient & reconfigurable FPGA and ASIC implementation of a spectral Doppler ultrasound imaging system., and . ASAP, page 198-202. IEEE Computer Society, (2013)An Energy-Efficient Programmable Manycore Accelerator for Personalized Biomedical Applications., , , , , , and . IEEE Trans. Very Large Scale Integr. Syst., 26 (1): 96-109 (2018)Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods., , , and . FLAIRS, AAAI Press, (2014)Live demonstration: Towards an ultra low power on-board processor for Tongue Drive System., , , , , and . BioCAS, page 1. IEEE, (2015)SCALENet: A SCalable Low power AccELerator for Real-time Embedded Deep Neural Networks., , and . ACM Great Lakes Symposium on VLSI, page 129-134. ACM, (2018)An ultra low power feature extraction and classification system for wearable seizure detection., , , and . EMBC, page 7111-7114. IEEE, (2015)A low power seizure detection processor based on direct use of compressively-sensed data and employing a deterministic random matrix., , , , and . BioCAS, page 1-4. IEEE, (2015)