Managed by UT-Battelle for the Department of Energy 1 Advanced Brain-Wave Analysis For Early Diagnosis of Alzheimer’s Disease (AD) Presented by Jaron Murphy

Managed by UT-Battellefor the Department of Energy

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Advanced Brain-Wave Analysis For Advanced Brain-Wave Analysis For Early Diagnosis of Alzheimer’s Early Diagnosis of Alzheimer’s

Disease (AD)Disease (AD)

Presented byJaron Murphy

Research Alliance in Math and ScienceDr. Lee Hively & Dr. Nancy Munro

Computational Sciences and Engineering

August 13, 2008Oak Ridge, Tennessee


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Overview

Background

Purpose

Research Objectives

Implementation

Current Status

Challenges

Future Applications


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What is Alzheimer’s 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 Alzheimer’s 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 – Alzheimer’s 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 Alzheimer’s Association


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Purpose

Improve detection of early diagnosis of Alzheimer’s disease and related diseases (ADRD)

Develop a portable software that will execute on supercomputers as well as PDA’s, 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 Sneddon’s 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

brain’s 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 SourceAnterior 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 Sneddon’s 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:• Parkinson’s 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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Questions

19 Managed by UT-Battellefor the Department of Energy

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Managed by UT-Battelle for the Department of Energy 1 Advanced Brain-Wave Analysis For Early Diagnosis of Alzheimer’s Disease (AD) Presented by Jaron Murphy