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

Kyong Ho Lee; Sun Yuan Kung; Naveen Verma

doi:10.1109/ICASSP.2011.5946802

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

Kyong Ho Lee, Sun Yuan Kung, Naveen Verma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 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 language	English (US)
Title of host publication	2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages	1597-1600
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2011.5946802
State	Published - 2011
Event	36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic Duration: May 22 2011 → May 27 2011

Publication series

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

Other

Other	36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/Territory	Czech Republic
City	Prague
Period	5/22/11 → 5/27/11

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

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

Access to Document

10.1109/ICASSP.2011.5946802

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). Article 5946802 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946802

Lee, Kyong Ho ; Kung, Sun Yuan ; Verma, Naveen. / Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications. 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings. 2011. pp. 1597-1600 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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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.",

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year = "2011",

doi = "10.1109/ICASSP.2011.5946802",

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isbn = "9781457705397",

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booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",

note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",

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Lee, KH, Kung, SY & 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., 5946802, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1597-1600, 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, Prague, Czech Republic, 5/22/11. https://doi.org/10.1109/ICASSP.2011.5946802

Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications. / Lee, Kyong Ho; Kung, Sun Yuan ; Verma, Naveen.
2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings. 2011. p. 1597-1600 5946802 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Lee KH, Kung SY , Verma N. 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. 2011. p. 1597-1600. 5946802. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2011.5946802

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

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All Science Journal Classification (ASJC) codes

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