A 1.52 uJ/classification patient-specific seizure classification processor using Linear SVM

@article{Altaf2013A1U,
  title={A 1.52 uJ/classification patient-specific seizure classification processor using Linear SVM},
  author={Muhammad Awais Bin Altaf and Jerald Yoo},
  journal={2013 IEEE International Symposium on Circuits and Systems (ISCAS2013)},
  year={2013},
  pages={849-852}
}
This paper presents an 8-channel electroencephalograph (EEG) classification processor for seizure detection and recording. To integrate 8 channels, an area- and energy-efficient filter architecture using Distributed Quad-LUT (DQ-LUT) is proposed, which reduces area by 64.2% with minimal overhead in power-delay product. The on-chip patient specific classification with a Linear Support-Vector Machine (SVM) results in 82.7% seizure detection accuracy with a 2 second latency using the CHB-MIT EEG… CONTINUE READING

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  • To integrate 8 channels, an area- and energy-efficient filter architecture using Distributed Quad-LUT (DQ-LUT) is proposed, which reduces area by 64.2% with minimal overhead in power-delay product.

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