Reliable health monitoring: a commercial off-the-shelf and a field programmable hardware approach
Cheriyan, Ajay M.
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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
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 %.
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