Advanced Brain-Wave Analysis For Early Diagnosis of Alzheimer

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Advanced Brain-Wave Analysis For Early Diagnosis
of Alzheimers Disease (AD)
Presented by Jaron Murphy Research Alliance in
Math and Science Dr. Lee Hively Dr. Nancy Munro
Computational Sciences and Engineering August
13, 2008 Oak Ridge, Tennessee
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Overview

• Background
• Purpose
• Research Objectives
• Implementation
• Current Status
• Challenges
• Future Applications

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What is Alzheimers disease (AD)?

• AD is a neurodegenerative disease of the nervous
  system that
• Affects the cognitive abilities of a person
• Renders the person functionally useless in
  society
• Progressive worsens over time and is fatal
• Is presently incurable

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Symptoms

• Symptoms that may occur in the early stage of AD
• Confusion
• Short-term memory disruption or loss
• Problems with attention and spatial orientation
• Personality changes
• Language difficulties
• Unexplained mood swings

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AD Facts

• According to the 2008 Alzheimers Disease Facts
  and Figures
• As many as 5.2 million people in the United
  States are living with AD
• 10 million baby boomers will develop AD in their
  lifetime
• Every 71 seconds, someone develops AD
• Alzheimers is the 6th leading cause of death in
  the United States, surpassing diabetes reported
  by the Centers for Disease Control and Prevention
  on June 12, 2008
• Direct and indirect costs of Alzheimer's and
  other dementias to Medicare, Medicaid and
  businesses amount to more than 148 billion each
  year
• Published by the Alzheimers Association

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Purpose

• Improve detection of early diagnosis of
  Alzheimers disease and related diseases (ADRD)
• Develop a portable software that will execute on
  supercomputers as well as PDAs, cellular
  devices, and other mobile equipment without
  modifying original coding

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Research Objectives

• Implement qEEG Methodology of Dr. Shankle and
  Sneddon in Java
• Analyze University of Kentucky EEG data to
  determine if Shankle and Sneddons results can be
  confirmed
• Demonstrate early detection of Diffuse Lewy Body
  disease (DLB) for the first time via qEEG
• Causes cognitive problems similar to AD and motor
  problems like those in Parkinson's
• Incurable and progressive disease like AD

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qEEG Methodology

• Electroencephalography (EEG)
• the scalp recording of the brains electrical
  activity
• Quantitative EEG (qEEG) method
• Developed by Dr. Robert Sneddon and Dr. William
  Shankle (University of California, Irvine)

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Delayed Recognition Tasks

• Two delayed recognition tasks, each consisting
  of
• Working memory task
• Display sets of 2 visual stimulus at a time 10
  sets total
• Subjects must indicate (yes/no) whether stimuli
  match
• Recognition memory task
• Presents 20 visual stimuli 10 from the working
  memory task
• Subjects must indicate whether a given stimuli
  was shown in the WMT

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• Analysis consists of data from 4 channels that
  correspond to movement of information in the
  brain
• Anterior Channels AF3 and AF4
• Posterior Channels P3 and P4

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Data Source
Anterior Channels
Posterior Channels
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• Sneddon and Shankle hypothesize that a normal
  brain would create a higher level of information
  after integrating incoming sensory information,
  than brains with ADRD

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Java Program Design

• Divided code into three classes
• Data reader class
• Read and allocates space for the data
• qEEG Calculations class
• Performs the critical points, variance, and ratio
  calculations
• Data Artifact filter class
• Filters out artifacts such as eye blinks and
  muscle movement

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Current Status

• Debugging the dataReader and qEEGCalc classes
• Translating dataFilter code from FORTRAN to Java
• Calibrating parameters of data input

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Challenges

• Developing code structure
• Divide code into various tasks
• Determine the function of those tasks
• Figure out how those tasks will communicate
  together
• Deciphering Sneddons code
• Finding the maxima and minima
• Non-linear analysis of data
• Analyzing gigabytes of data

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Future Applications

• Anticipate a clinical device in the next 5 to 10
  years that could be used by a physician to
  provide early diagnosis of AD in 5 months before
  AD onset
• Ability to provide early diagnosis of
  neurological diseases
• Parkinsons disease
• Diffuse Lewy Body disease
• Clinical Depression
• Bi-Polar Disorder

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Collaborations

• Dr. Yang Jiang, University of Kentucky School of
  Medicine - EEG Data Samples
• Dr. Robert Sneddon, University of California,
  Irvine Tsallis Entropy - MatLab Code

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Acknowledgments

• The Research Alliance in Math and Science program
  is sponsored by the Office of Advanced Scientific
  Computing Research, U.S. Department of Energy.
• The work was performed at the Oak Ridge National
  Laboratory, which is managed by UT-Battelle, LLC
  under Contract No. De-AC05-00OR22725. This work
  has been authored by a contractor of the U.S.
  Government, accordingly, the U.S. Government
  retains a non-exclusive, royalty-free license to
  publish or reproduce the published form of this
  contribution, or allow others to do so, for U.S.
  Government purposes.
• I would like to thank George Seweryniak for
  sponsoring RAMS and giving students like myself
  the opportunity to venture into the realm of
  research and Mrs. Debbie McCoy for managing the
  RAMS program even through her times of hardship.
• I would also like to thank my mentors for their
  guidance and advice during my research.

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Any Questions or Comments?
Questions
19 Managed by UT-Battellefor the Department of
Energy
UTBOG_Computing_0801