University of Illinois Urbana-Champaign

Reliable health monitoring: a commercial off-the-shelf and a field programmable hardware approach

Cheriyan, Ajay M.

Content Files
1_cheriyan_ajay.pdf

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https://hdl.handle.net/2142/16175

Description

Title
Reliable health monitoring: a commercial off-the-shelf and a field programmable hardware approach
Author(s)
Cheriyan, Ajay M.
Issue Date
2010-05-19T18:39:50Z
Director of Research (if dissertation) or Advisor (if thesis)
Iyer, Ravishankar K.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2010-05-19T18:39:50Z
Keyword(s)
  • Commercial off-the-shelf (COTS)
  • Field-Programmable Gate Array (FPGA)
  • Electroencephalography (EEG)
  • Seizure
  • Microcontroller
  • MSP430
  • Leon3
  • Advanced Microcontroller Bus Architecture (AMBA)
Abstract
With the tremendous advancements in low cost, power-efficient hardware and the recent interest in biomedical embedded systems, numerous traditional biomedical systems can be replaced with smaller and faster embedded systems that perform real-time analysis to provide bio-feedback to the users. This thesis takes a look at two hardware implementations – one using commercial off-the-shelf (COTS) components and the other using field programmable logic. The focus of the design was to ensure a portable, inexpensive, power-efficient and robust device that could perform analysis of physiological signals, which would in turn help alert the user in the event of an abnormality. The COTS hardware implementation provided the framework using a microcontroller as the processing element for a reliable health monitoring device with a seizure detection directly embedded in it. The field programmable gate array (FPGA) platform based implementation was proposed and simulated to overcome the two disadvantages of the COTS approach – the inability to support customization of the device to suit the end-user’s monitoring requirements and complex detection schemes requiring significant processing capability. The FPGA platform was simulated first as a standalone module and later as part of an SoC design. The novel algorithm included a feature extraction phase and a machine learning based seizure detection phase. Simulation based testing of the device showed a detection accuracy of 99.2 %.
Graduation Semester
2010-5
Permalink
http://hdl.handle.net/2142/16175
Copyright and License Information
Copyright 2009 Ajay Mathews Cheriyan

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