Statistical signal processing for an implantable ethanol biosensor.

Jae Joon Han, Peter C. Doerschuk, Saul B. Gelfand, Sean J. O'Connor

Research output: Contribution to journalArticle

Abstract

The understanding of drinking patterns leading to alcoholism has been hindered by an inability to unobtrusively measure ethanol consumption over periods of weeks to months in the community environment. Signal processing for an implantable ethanol MEMS bio sensor under simultaneous development is described where the sensor-signal processing system will provide a novel approach to this need. For safety and user acceptability issues, the sensor will be implanted subcutaneously and therefore measure peripheral-tissue ethanol concentration. A statistical signal processing system based on detailed models of the physiology and using extended Kalman filtering and dynamic programming tools is described which determines ethanol consumption and kinetics in other compartments from the time course of peripheral-tissue ethanol concentration.

Original languageEnglish (US)
Pages (from-to)3704-3707
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2006
Externally publishedYes

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Biosensing Techniques
Biosensors
Signal processing
Ethanol
Sensors
Micro-Electrical-Mechanical Systems
Tissue
Physiology
Dynamic programming
Alcoholism
Drinking
MEMS
Safety
Kinetics

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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