Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting these in intelligent, closed-loop systems requires detecting specific physiological states using very low power (i.e., 1-10mW for wearable devices, 10-100μW for implantable devices). Machine learning is a powerful tool for modeling correlations in physiological signals, but model complexity in typical biomedical applications makes detection too computationally intensive.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages1597-1600
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • biomedical devices
  • energy efficiency
  • kernel-energy trade-off
  • machine learning

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  • Cite this

    Lee, K. H., Kung, S-Y., & Verma, N. (2011). Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 1597-1600). [5946802] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946802