Real-Time Processing of EEG Signals for Mobile Detection of Seizures

Colm Seale

B.E. Electronic & Computer Engineering Project Report

EE426

Supervisor: Prof. Edward Jones

Co-assessor: Dr. Gearóid Ó’Laighin

March 2012

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Abstract

The following is a Project Thesis titled “Real-Time Processing of EEG Signals for Mobile

Detection of Seizures“. Discussed in the report are the main aims and objectives of the project

and the results achieved and conclusions drawn throughout the course of the project. The

overall aim of the project is to design an algorithm that will read signal data from an EEG

source and process this data to determine if the person is undergoing an epileptic seizure. This

program will be ported to mobile devices. The aim is to find a balance between the

performance of the algorithm employed in detecting seizures and the amount of processing

required to calculate the presence of seizure. The project also aims to investigate the effect of

compression of EEG signal data on the overall effectiveness of the seizure detection algorithm.

This report will detail some of the methods and procedures taken for developing algorithms,

what milestones were reached and results from testing and development. The results

demonstrate the feasibility of implementing seizure detection on a smart phone platform, and

furthermore, significant compression of the EEG signal is possible without loss of diagnostic

data. Thus, the outcome of the project supports the possibility of significantly enhancing the

range of technology available to support mobile healthcare.

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Declaration of Originality

I declare that this thesis is my original work except where stated.

Date: ___________________________________

Signature: ___________________________________

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Acknowledgements

The author would like to acknowledge the help and support I received throughout the project

from my project supervisor Dr. Edward Jones.

The author would also like to thank Gary Higgins for his input and Dr. Liam Kilmartin for his

assistance at various points throughout the project.

This project would not have been possible without the support of my fellow 4th EE/ECE

classmates.

Finally, the author would like to thank my parents for their continued support over the last 4

years.

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Table of Contents

Abstract .......................................................................................................................................... i

Declaration of Originality .............................................................................................................. ii

Acknowledgements ..................................................................................................................... iii

Table of Figures ............................................................................................................................ vi

Figures...................................................................................................................................... vi

Tables ...................................................................................................................................... vii

Equations ................................................................................................................................ vii

Glossary ..................................................................................................................................... viii

1. Project Introduction .............................................................................................................. 1

1.1 Background ................................................................................................................... 1

1.2 Aims .............................................................................................................................. 1

1.3 Project Milestones ........................................................................................................ 2

1.4 Health and Safety Issues ............................................................................................... 2

1.5 Project Timeline ............................................................................................................ 3

1.6 Thesis Outline ............................................................................................................... 4

2. Project Components ............................................................................................................. 5

2.1 Project Hardware .......................................................................................................... 5

2.2 Project Software ........................................................................................................... 7

2.3 Data Sources ................................................................................................................. 9

3. Algorithm Development (MATLAB) .................................................................................... 12

3.1 Introduction ................................................................................................................ 12

3.2 Algorithm Design ........................................................................................................ 12

3.3 Algorithm Development ............................................................................................. 13

3.4 Classification in MATLAB ............................................................................................ 15

3.5 Pre-Processing ............................................................................................................ 17

3.6 Results ......................................................................................................................... 19

Linear Discriminate Analysis: .............................................................................................. 19

Support Vector Machine: .................................................................................................... 20

3.7 Conclusions ................................................................................................................. 20

4. Mobile System Development ............................................................................................. 21

4.1 Introduction ................................................................................................................ 21

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4.2 Java Development....................................................................................................... 21

4.3 Programs ..................................................................................................................... 22

3.5 Emotiv EPOC Development ........................................................................................ 26

3.6 Results ......................................................................................................................... 28

3.7 Conclusions ................................................................................................................. 29

5. Compression of EEG Data ................................................................................................... 30

5.1 Introduction ................................................................................................................ 30

5.2 Method of Study ......................................................................................................... 30

5.3 Results ......................................................................................................................... 32

5.4 Conclusions ................................................................................................................. 33

6. Discussion ........................................................................................................................... 34

6.1 Introduction ................................................................................................................ 34

6.2 Discussion ......................................................................................................................... 34

6.3 Conclusion................................................................................................................... 35

References .................................................................................................................................. 37

Appendix ..................................................................................................................................... 39

A.1 HTC Wildfire Specifications: ............................................................................................. 39

A.2 MATLAB Filter Code .......................................................................................................... 39

A.3 Wildfire Bluetooth Code Problem .................................................................................... 40

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Table of Figures

Figures

Figure 1-1: Gantt chart of Project Timeline .................................................................................. 3

Figure 2-1: MATLAB processing EEG data..................................................................................... 8

Figure 2-2: Image demonstrating the different areas in an Ictal signal file taken from the

Freiburg Database....................................................................................................................... 10

Figure 3-1: Block Diagram of Seizure Algorithm ......................................................................... 12

Figure 3-2: Graphs showing EEG Ictal signal [top]; power spectra [second from top]; Shannon

[centre] and Kullback-Leibler [second from bottom] entropies; Relative Band Ratios [bottom]

.................................................................................................................................................... 13

Figure 3-3: Visual representation of EEG data for the raw signal and Hjorth parameters......... 15

Figure 3-4: LDA Classifier Performance of Raw Signal compared to Pre-Processed Signal ........ 17

Figure 3-5: SVM Classifier Performance results of Raw Signal compared to Pre-Processed ..... 17

Figure 3-6: A visual example of a pre-processing algorithm for raw EEG signals for seizure ..... 18

Figure 3-7: Raw EEG (Top) vs. Filtered EEG (Bottom) ................................................................. 18

Figure 3-8: Frequency Response of MATLAB Band-Pass Filter ................................................... 19

Figure 4-1: Flowchart showing high-level design ....................................................................... 24

Figure 4-2: Flowchart of Real-Time System Loop ....................................................................... 24

Figure 4-3: Application Screen Captures; Application requesting Bluetooth [left], Scanning for

devices [centre], Classifying data [right] .................................................................................... 26

Figure 4-4: Results output of the Naïve-Bayes Classifier using the evaluateModel function from

WEKA libraries ............................................................................................................................ 28

Figure 4-5: PowerTutor measurements of Seizure Detection App; Power usage over a period of

10 seconds [left] and Power usage as a percentage of whole application [left] ........................ 29

Figure 5-1: Comparison of EEG Signal at varying CR .................................................................. 31

Figure 5-2: Pseudo-Code for calculation of CR ........................................................................... 31

Figure 5-3: Percentage Root-Mean-Squared Deviation vs. Compression Ratio using SPIHT

Compression ............................................................................................................................... 32

Figure A-1: Code used to design MATLAB Filter Coefficients ..................................................... 39

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Tables

Table 2-1: Comparison of two available consumer EEG Hardware options ................................. 6

Table 2-2: Comparison of EEG Data Sources .............................................................................. 11

Table 3-1: Results of LDA Classifier on Original Signals ............................................................. 19

Table 3-2: Results of LDA Classifier on Original Signal ................................................................ 20

Table 5-1: Performance of LDA Classifier vs. CR ......................................................................... 32

Table 5-2: Performance of SVM Classifier vs. CR ........................................................................ 33

Table A-1: Specifications of HTC Wildfire. .................................................................................. 39

Equations

Activity .................................................................................................................................................. 14

Mobility ................................................................................................................................................. 14

Complexity ............................................................................................................................................ 14

Sensitivity .............................................................................................................................................. 16

Specificity .............................................................................................................................................. 16

Correct Rate .......................................................................................................................................... 16

Standard Deviation ............................................................................................................................... 21

Derivative of a Signal ............................................................................................................................ 22

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Glossary API – Application programming Interface

CR – Compression Ratio

CSV – Comma Separated Values

EEG – Electroencephalography

JNA – Java Native Access

JNI – Java Native Interface

IDE – Integrated Development Environment

LDA – Linear Discriminate Analysis

PRD – Percentage Root mean Squared Distortion

SDK – Software Development Kit

SPIHT – Set Partitioning in Hierarchical Trees

SVM – Support Vector Machine

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1. Project Introduction

1.1 Background

With the increasing cost and demands made of healthcare infrastructure and services, there is

significant interest in the development of technology that can help to detect medical

problems at an early stage, where successful (and lower cost) intervention is more likely. In

particular, there is growing interest in the development of “wearable” or “ambulatory”

systems for monitoring and analyzing various biological signals. A key requirement of such

systems is that they operate with low power to permit extended battery life.

A commonly-used framework for the implementation of such systems (particularly where

more than one sensor may be employed) is a so-called “Body Area Network” (BAN), which

consists of a network hub, to which one or more “smart sensors” are connected (ideally

wirelessly, for unrestricted movement by the wearer). Signal and data processing may be

divided between the smart sensors and the hub, depending on how computational power is

distributed in the BAN (Body Area Networks typically also incorporate coding and encryption

for data privacy, however, this functionality is beyond the scope of this project).

1.2 Aims

This project will investigate the development and implementation of a number of signal

processing algorithms for epileptic seizure detection based on signal processing of

Electroencephalogram (EEG) signals, in the mobile digital health environment. Firstly, this will

involve DSP algorithm development (in MATLAB) in order to build a system for analysis and

detection of seizures. Secondly, the project will aim towards implementation of some of the

DSP algorithms in a simulated BAN, using an off-the-shelf Emotiv EEG Headset

(http://www.emotiv.com) communicating with an Android mobile phone acting as a network

“hub” (because of restrictions imposed by the development environment, it may be necessary

to “emulate” a direct connection between the headset and the phone). The project will also

aim to investigate the effects of signal compression on seizure detection performance. For

test and development purposes, publicly-available databases of EEG signals will be used,

particularly the University of Freiburg EEG database [1]. Where real-time testing is being

carried out using software implemented on the Android platform, this will be restricted to use

with normal healthy subjects.

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1.3 Project Milestones

Pass

Implementation of feature extraction and classification algorithms for seizure detection

(in MATLAB), using Freiburg EEG database.

Average

Translation of feature extraction and classification algorithms into C or Java, and

validation of performance.

Development of a basic Android application to gain familiarity with software

development on this platform.

Good

Development of communications infrastructure (indirect via laptop) to enable

communication between Emotiv headset and Android mobile phone.

Characterization of computational complexity of feature extraction on Android platform.

Very Good

Implementation of real-time or near real-time seizure detection on Android platform, and

validation using healthy test subject(s).

Excellent

Investigation of the impact of compression on seizure detection performance.

Analysis of potential power savings to be gained through the use of compression.

1.4 Health and Safety Issues

There are no health and safety issues. The project will largely involve software development

on PC and Android platforms. The Emotiv EEG headset is an approved part, and will not be

used other than in accordance with the manufacturer’s recommendations.

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1.5 Project Timeline

Figure 1-1: Gantt chart of Project Timeline

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1.6 Thesis Outline

The main text of this thesis is split into 4 main chapters, a discussion and a conclusion; Project

Components, Algorithm Development (MATLAB), Mobile System Development and

Investigation of Compression on EEG. The first chapter outlines the major system components

used in the design and development for each of the following chapters. This chapter provides

a detailed background into each major non-standard hardware and software project

components. The following two main chapters (3-4) will detail the design and development of

the seizure detection algorithm on PC and Smartphone. These chapters begin by outlining the

necessary background and major aims for the chapter. Following this, these chapters will

focus on the design, development and testing processes of their respective objectives. Each

chapter will conclude with the results obtained throughout the course of the chapter’s

development and conclude with a brief summary. The fifth chapter includes a small scale

study on the effects of Compression of EEG. The final discussion and conclusion of the main

text will bring together the results from the 3 main chapters and draw conclusions whilst

outlining areas for future research, and will provide a critique of the project itself including

areas such as Project Management etc.

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2. Project Components

2.1 Project Hardware

Emotiv EPOC

The Emotiv EPOC is a high resolution, neuro-signal acquisition and processing wireless neuro

headset. The Emotiv Headset is a peripheral originally designed for gaming purposes by

detecting conscious thought, emotions and head rotation. It can also be used to collect raw

EEG data from users. The Headset features include:

14 saline sensors which are places along the scalp in order to read signals from

different areas of the brain.

Gyroscopes for head rotation and tilt

Bluetooth connection to PC

Software Suites which allow the connection to each sensor to be tested and the raw

EEG can be monitored from a test bench program.

The headset documentation includes programs written in C++ which allows the user to Log the

preprocessed EEG data from the headset. Also included was a JNA Wrapper (Java Native

Access) program which would allow users to write Java applications to access this EEG data.

This “Wrapper” works by allowing Java programs to access native DLL libraries on a machine

while sacrificing the program portability that Java offers, but this approach encountered issues

which will be discussed later in this thesis.

This particular headset provides a cheap and affordable platform for portable wireless capture

of EEG data. However, the Emotiv EPOC headset has some limitations which would affect how

this project progressed. The Emotiv EPOC headset is only compatible with Windows PC and

the EPOC Headset itself is not programmable. Therefore in order to capture real-time data

and process this data on a mobile device, a method had to be devised of transferring the raw

data over wireless connection to the handheld.

Numerous approaches were investigated towards this, due to the time-limited nature of this

project, only a partial solution was found, details on which are discussed below.

At the close of work on this project, the method for transferring real data from the Emotiv EEG

headset is as follows:

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A C++ program is run on PC which reads data from the connected Emotiv EPOC

Headset. It is a modified version of the C++ provided in the support

documentation provided by Emotiv Systems. This data is saved to a CSV (Comma

Separated Values) file which is placed in a common directory between the C++

and Java programs.

The Java program is manually run afterwards, which reads the data from the CSV

file, and buffers this data into window frames with no overlaps, and transfers this

data to phone over Bluetooth.

EEG features are extracted from each of these frames of data and classified as

either non-Ictal or Ictal frames.

Other methods of transferring the data included the creation of a C++ to Java client-server

socket connection, or JNI (Java Native Interface, an alternative to JNA) which would also allow

the java program to directly access the native DLL libraries in order to directly connect with

the headset.

Another option available is to use a headset with compatibility with the Android mobile

devices. This would in the long-term, be a more desirable and more practical solution. A

comparison of the Emotiv EPOC Headset and the Android/iOS compatible XWave Headset is

given in Table 2-1.

Device Cost Electrodes SDK Android iPhone

Emotiv EPOC $300 14 Yes No No

XWave $90 1 Yes Yes Yes

Table 2-1: Comparison of two available consumer EEG Hardware options

Android Mobile Device

The Android mobile platform is an open source software platform developed by Google, based

off the Linux Kernel. Development of Android Applications is generally through Java using

Google developed java libraries. Android is available under an open-source license since

October 21st, 2008. As it is open-source, all the software and documentation required to build

the desired application is free and therefore it is the platform of choice for the development

lifecycle of this project. Android OS also provides hardware support for Bluetooth which will

the method of choice for establishing wireless connections between EEG Source and the

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Mobile Device. The hardware model of Android device used through this project was the HTC

Wildfire Smartphone whose specs relevant to this project are detailed in Appendices A1.

Programming of the Android device was carried out in the Eclipse IDE with the Android SDK

Plug-in installed. The Eclipse IDE provides an Android Virtual Device which allows limited

functionality for debugging Android programs without requiring the use of a physical Android

device.

Bluetooth v3.0

Bluetooth is an open wireless technology standard for transmitting data over short distances

between Bluetooth enabled devices, enabling the creation of Personal Area Networks (PANs).

Several devices can be connected to one Bluetooth server device, which opens up the

possibility for using a device such as Android Mobile Devices to act as the central hub in order

to create the Body Area Network (BAN) which would allow a user to monitor biofeedback

from various sensors. Bluetooth v3.0 supports up to 24 Mbit/sec theoretically, which is

beyond adequate for the purposes of this project.

2.2 Project Software

MATLAB

MATLAB (shown running on windows 7 in Figure 2-1) is a high level technical computing

language and graphical interface used for intensive mathematically computations. It is

designed to be more efficient and more accurate than typical programming languages like C++

and Java. It provides users with various tools for data analysis and visualization, and will be the

primary tool used in this project for accessing the effectiveness and sensitivity of any

developed algorithms. The software also provides various toolboxes designed for use with

specific parts of this project, which are further detailed below.

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Figure 2-1: MATLAB processing EEG data

Signal Processing Toolbox:

The Signal Processing toolbox provides tools for analyzing and developing algorithms for

digital signal processing. It includes tools for analyzing in time and frequency domains,

computation of FFTs etc. The signal processing toolbox was used for design of FIR filters for

this project.

Bioinformatics Toolbox:

The Bioinformatics Toolbox includes SVM (Support Vector Machine) which was one algorithm

used to classify frames of data to their individual Ictal and Non-Ictal states.

Statistics Toolbox:

The Statistics Toolbox includes a simple Naïve- Bayes Linear Discriminate Classifier (LDA) which

was another investigated algorithm used to classify frames of data to their individual Ictal and

Non-Ictal states.

WEKA

In order to develop classifiers in Java, data mining and data processing jar libraries developed

by the University of Waikato (dubbed WEKA Libraries) were employed to the program in order

to provide the necessary tools to continue development.

WEKA is a collection of machine learning algorithms for data mining tasks. The algorithms can

either be applied directly to a dataset or called from your own Java code. WEKA contains tools

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for data pre-processing, classification, regression, clustering, association rules, and

visualization. It is also well-suited for developing new machine learning schemes.

WEKA is open source software issued under the GNU General Public License [2].

WEKA provides full documentation and support online. WEKA Linear Classifiers are employed

in both the PC and Android Java programs for classification of seizure data.

BlueCove API

BlueCove is a Java library which provides an interface for java programs with Bluetooth stacks

found in Windows, Mac OS and other operating systems.

SPIHT Compression

For the latter part of the project, investigation was carried out into the effect that

compression algorithms on the quality of the results obtained from the classifier models. First

the signal is compressed to a desired compression ratio. Following this the compressed

undergoes decompression, before entering the preprocessing and feature extraction phases.

The results are then compared with that of the original signal (see Section 9: Compression on

EEG Signals). Compression will be carried out using the ‘Set Partitioning in Hierarchical Trees’

(SPIHT) Algorithm.

SPIHT Compression is a form of “lossy” compression, i.e. a certain amount of data is lost in the

compression process. SPIHT Compression of a signal involves splitting a signal up into window

frames, and calculating the Discrete Wavelet Transform (DWT) at each window. This converts

a frame of samples to a sequence of bits. These bits are stored in a vector and organized in

order of most important bits first. Compression is then carried about by simply truncating the

bit sequence by the desired compression ratio (CR) and retaining the most important bit

sequence. For example, for a compression ratio of 4:1, only the first 25% of bits are retained.

This report investigates the Percentage Root Mean Squared Distortion (PRD) of a signal at

different CR values, and compares the output of the classifier to that of the original signal.

2.3 Data Sources

Freiburg Seizure Database

The Freiburg database is a collection of EEG recordings from 21 patients suffering from

‘medically intractable focal epilepsy. The data was taken from intracranial EEG recordings

conducted by the Epilepsy Center of the University Hospital of Freiburg. The strict test

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environment which this data was recorded ensures that the signals collected in the EEG

database have high signal-to-noise ratios and few artifacts.

The data itself was captured at a rate of 256Hz and quantized to 16bits and has not undergone

any sort of pre-processing. The records are saved as ‘ASC’ files, and each file consists of a 1-

dimensional array of data representing data recorded in mV from one EEG channel. The

recordings are split into 2 classes:

Ictal: These files are 1 hour long containing epileptic seizures and at least 50

minutes of pre-Ictal data.

Inter-Ictal: Data files consisting of 24 hours worth of Non-Ictal data.

The signal data itself into 2 different class groups, ‘Icital’ (Seizure EEG) and ‘Non-Icital’ (Non-

Seizure EEG). Figure 2-2 is a signal from an Ictal class data file from the Freiburg Database,

with an overlay demonstrating which section of data is Ictal or Non-Ictal.

Figure 2-2: Image demonstrating the different areas in an Ictal signal file taken from the Freiburg Database.

Live EEG Data

Live EEG Data for this project was recorded from the Emotiv EPOC headset mentioned earlier

in this paper. Using a modified version of C++ code provided in the documentation for the

EPOC Headset, EEG readings are recorded from the headset at a rate of 128Hz and save these

recordings in CSV format.

The recordings are usually between 30-seconds to a minute long where the user is in a relaxed

state. This was to reduce the effect that artifacts would have on the seizure detection

algorithm. It is important to note that for this project, the aim was not to create an application

which would be ready for use by a patient, but to investigate the possibility of being able to

use the processing power of modern Smartphone technology to detect seizures. Therefore,

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this report focuses on the ability of the Smartphone to classify seizure data in or close to real

time. For health and safety reasons outlined earlier, no live seizure data was captured from

epileptic patients using the EEG Headset. This is a serious limitation in the validation of the

software’s performance, as it is only possible to validate the ability of the software to correctly

detect Non-Ictal data.

Table 2-2 demonstrates the important differences between the data collected locally from the

Emotiv Headset and data gathered from the Freiburg database.

Freiburg Database Emotiv EPOC

Sampled at 256Hz Sampled at 128Hz

16 bit resolution 16 bit resolution

Invasive Non-Invasive

No preprocessing Preprocessed

Features Ictal data No Ictal data

Table 2-2: Comparison of EEG Data Sources

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3. Algorithm Development (MATLAB)

3.1 Introduction

This section details the design and implementation of a preliminary classification algorithm in

MATLAB, with the intention of carrying this algorithm forward through to development of the

Android Application. Discussed in this chapter are what parameters and features of EEG

signals were used in the classification of seizure activity, what classification algorithms were

investigated, how the algorithms respective performance was measured and how the

respective algorithm performance was improved relating to each classification algorithm.

3.2 Algorithm Design

The intention during this section of the project was to design the simplest system possible for

initial Android development and iteratively increase the complexity and computational cost of

the system to find the best balance of Algorithm Complexity vs. System Performance. Figure 3-

1 is a sample block diagram of how the system was expected to work at a high level. X(n)

represents the original signal; R(n) represents the preprocessed signal, and Y0 … Yn represent

features extracted from window frames of the preprocessed signal R.

X Band-Pass

Filter

Feature

ExtractionClassify

featrues

X(n) R(n)Y0(m), Y1(m) ...

Yn(m)

Figure 3-1: Block Diagram of Seizure Algorithm

Throughout the design it was important to keep in mind that direct porting of code from

MATLAB to Java is impossible. MATLAB is a high level computing language, which includes

toolboxes for dealing with a wide array of mathematical functions. In order to port these to

java, individual functions would have to be written in java for each MATLAB function used in

the ported algorithm.

Also, the Java programming language is poorly optimized for Digital Signal Processing. This is

because java is a high level programming language in itself. Unlike C++, Java does not allow for

lower level management of memory and pointers in order to make the code more efficient.

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This became important when attempting to choose the features which would be extracted in

order to classify EEG signals. For example, calculating the FFT of a windowed signal is much

more time consuming and processor intensive than calculating standard deviation.

3.3 Algorithm Development

Initial development of algorithms began on one file featuring Ictal data taken from the

Freiburg database. This data was loaded into MATLAB and tested on code to extract a number

of different features. The original code calculates the Windowed Fourier Transform at a frame

size of 1024 samples per frame with no window overlap and then calculates and plots the

power spectra and the Shannon and Kullback-Leibler (relative) entropies. It also calculates the

relative band ratio (RIR). [3] The calculations taken and plotted over patient seizure data in

order to get a visual representation for the changes that occur in these EEG features during

the onset of seizure as shown in Figure 3-2. However, some of these calculations are rather

complex to port over to java code. Efforts were made to look at alternative options for

calculating possible seizure activity with less computational cost and complexity.

Figure 3-2: Graphs showing EEG Ictal signal [top]; power spectra [second from top]; Shannon [centre] and

Kullback-Leibler [second from bottom] entropies; Relative Band Ratios [bottom].

Research conducted in the paper ‘Epileptic Seizure Detection: A Non-Linear Viewpoint’ and its

examination of time-dependant techniques of EEG analysis, it was apparent the basis for the

simple seizure detection algorithms would be to calculate the Hjorth Parameters [4].

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Using the code described above as a base, M files was developed to calculate the Hjorth

Parameters; Activity ( ), Mobility ( ) and Complexity ( ) and plot these values to

graphs. The first parameter ‘ACTIVITY’ is a measure of the mean power representing the

activity of the signal. The second parameter is an estimate of the mean frequency and is called

the ‘MOBILITY’. The last parameter, ‘COMPLEXITY’ gives an estimate of the bandwidth of the

signal. Since the calculation of Hjorth parameters is based on variance, the computational cost

of this method and its complexity is considered low in comparison to other methods[5][6].

The discrete time formulas for the calculation of these parameters are on the following page.

Activity:

3-1

Mobility:

3-2

Complexity:

3-3

Where represents the variance of the signal x, and represents the variance of the first

derivative of signal x, and represents the variance of the second derivative of signal x.

Window frames of 1024 (i.e. 4 seconds of data) samples were originally used to form the

feature vectors from which the values from Activity and Complexity would be calculated.

There was no overlap between each individual frame. For each frame of data, the values for

Activity and Complexity were calculated as per the formulae above.

The resulting visual representations demonstrated in Figure 3-3 show clear distinguishing

features during Ictal periods, demonstrating a large spike in Activity while conversely showing

a drop in Complexity to almost zero for the duration of the Ictal period. Visually these are solid

indicators of seizure activity under test conditions. However, the Mobility of the signal does

not show a definite change between Seizure and Non-Seizure states, and thus this feature was

not used when classifying the signal.

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Thus it was assumed at this point in the project that the Hjorth Parameters would be the

easiest to implement in Java code and later convert to the Android platform, and thus will

provide the basic benchmark for the system.

Figure 3-3: Visual representation of EEG data for the raw signal and Hjorth parameters

3.4 Classification in MATLAB

Following supervisor advice, it was decided to conduct research into two different

classification algorithms:

Linear Discriminate Analysis

Support Vector Machines.

Analysis of the 2 classification algorithms was carried out in MATLAB by training the linear and

SVM classifier models on identical sets of training data. Training data consisted of the pre-

calculated Activity and Complexity, as well as the equivalent group data indicated if each

frame represents either Ictal or Non-Ictal data.

Utilizing the Statistics and Bioinformatics Toolbox in MATLAB, classifiers were created, trained

and evaluated through cross-validation of data with a dataset split of 10/90 (10% test, 90%

training data). Comparison tests for classifiers were carried out with raw data features vs. pre-

processed feature extraction (Discussed later in this report). The results of the MATLAB

Classifiers can be viewed below.

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When evaluating the performance of a given Classifier, three values are taken into account.

‘CorrectRate’ is the percentage value of frames within a sample which were correctly classified

when compared to previously known results (i.e. the percentage of true positives and true

negatives of classified samples). Sensitivity relates to the test's ability to identify positive

results. Specificity relates to the ability of the classifier to identify negative results. When

attempting to create classifier models and choose between them, there is often a trade-off

between the Sensitivity and Specificity. Classifier performance in MATLAB is analyzed through

the classperf function. This function compares the results of the classifier on a set of test data

with the real group labels of the test data.

3-4

3-5

3-6

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Figure 3-4: LDA Classifier Performance results of Raw Signal compared to Pre-Processed Signal. The inclusion of a

band-pass filter increases the Classifiers performance by approx 6%, however the increase in sensitivity has lead

to a decline in specificity, a common trade off when designing classification algorithms.

Figure 3-5: SVM Classifier Performance results of Raw Signal compared to Pre-Processed Signal. The introduction

of a band-pass filter increases the Correct Rate by 2%.

3.5 Pre-Processing

Investigation was carried out to measure the effect of introducing pre-processing to the raw

EEG data before extracting features for seizure detection. Blanke et al. (2000) indicated that

the dominant Ictal frequencies exist between the ranges of 3Hz to 8.5Hz [7]. A sample DSP

algorithm from the aforementioned paper is featured in Figure 3-6. From A digital band pass

filter from 2Hz to 20Hz was implemented in MATLAB before carrying out the extraction and

classification programs to remove noise and artifacts (More detail in section titled ‘MATLAB

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Filters’). The results demonstrated a considerable increase in the specificity and sensitivity of

the LDA Classifier, as documented in Figure 3-4 and 3-5 above.

Figure 3-6: A visual example of a pre-processing algorithm for raw EEG signals for seizure detection taken from

Cecchin et. All 2010[8]

It should be noted here that all data being tested to date was sourced from 1 patient under

strict test conditions which minimized the effect of artifacts and noise in the EEG signals, and

such reliability being demonstrated using these features should not be expected in real-world

applications. For the moment, the project aims only to test the theoretical possibility of

implementing seizure detection on mobile phones. When approaching the problem of

capturing live EEG data from a mobile patient, there are many more factors to be taken into

account which impede the quality of the data received and the performance of the

classification algorithms. Figure 3.7 gives a visual example of the change in the raw data after

filtering.

Figure 3-7: Raw EEG (Top) vs. Filtered EEG (Bottom)

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MATLAB Filter Design

As mentioned earlier in this report, the dominant Ictal frequencies are believed to reside

between the 2 and 20Hz region. A simple filter was designed using MATLAB’s fir1 function of

order 50 with a band-pass between 2 and 20Hz in order to isolate these signal bands. The

code listing for the filter can be found at Appendix A.2. The MATLAB fir1 function creates a

digital filter using the window method. The filter was designed based on the following

parameters. (The code for the filter can be found in the ‘filterB.m’ function file found on the

disc provided with this report). The frequency response for the filter is plotted in Figure 3-8

using MATLAB's freqz function.

Figure 3-8: Frequency Response of MATLAB Band-Pass Filter

3.6 Results

Tables 3-1 and 3-2 contain results from tests run on the classifier models with data from the

Freiburg Database. The tables feature 3 the performance of the algorithms on 3 Ictal datasets

from the Freiburg Database and 2 Non-Ictal Datasets.

Linear Discriminate Analysis:

Dataset: 0015 (Ictal) 0018 (Ictal) 0021 (Ictal) 0017 0022

Correct Rate 0.9867 0.9855 0.9922 0.9911 0.98

Sensitivity 0.7988 0.9955 0.998 0.9999 0.011

Specificity 0.6129 0.5 0.7143 0.9911 0.9811

Table 3-1: Results of LDA Classifier on Original Signals

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Support Vector Machine:

Dataset: 0015 (Ictal) 0018 (Ictal) 0021 (Ictal) 0017 0022

Correct Rate 0.9121 0.5818 0.9055 0.6719 0.485

Sensitivity 0.9355 0.778 0.9575 0 0

Specificity 0.9113 0.5778 0.9015 0.6726 0.4855

Table 3-2: Results of LDA Classifier on Original Signal

3.7 Conclusions

The results for this section of the project demonstrate that overall, the LDA classifier was

significantly better than the SVM Classifier. Preprocessing the signal before classification

through a 2-20Hz band-pass filter increased the rate of correctly classified results by between

2-6%, whilst also affecting the sensitivity and specificity of each algorithm to different extents.

Taking this knowledge forward into the Mobile Development part of the project, it was

decided that the LDA classifier the Hjorth Parameter feature extraction would be

implemented on the Android platform for the first iteration of the Android Application.

However, the effect compression has on data classification is analyzed later in this paper and

includes the Support Vector Machine classifier model in its analysis.

It should be noted here that the EEG signals used for this part of the report were sourced

from the intracranial data of one individual patient in the database featuring little to no

artifacts, and high signal-to-noise ratio. In turn this means that the data used to develop the

algorithm was ‘ideal’ in nature and the resulting high classification rates should not be

expected with signals that are more complex in nature.

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4. Mobile System Development

4.1 Introduction

This section details the development process of the underlying java classification program

which formed the basis for porting the application to the Android Mobile Platform. Areas of

the project covered in this section are the application design, converting high-level Math

Programming Language code to Java processes and deploying this code to the Android OS

platform. Also covered is the integration of WEKA Libraries into the classifier application at

both the Java and Android levels, as well as a discussion on the use of Bluetooth and

integrating the Emotiv EPOC into the mobile development, and the issues thereof.

The overall aim of this part of the project was to determine if it is possible to implement a

simple seizure detection algorithm on a mobile platform which operates in or close to real

time. It was outside the scope of this project to develop an interactive UI for potential users to

monitor their own EEG state.

4.2 Java Development

The aim of this section of the project was simply to convert the code produced in MATLAB .m

files and replicate these codes in java. This involved manually writing Java code which

calculates basic values such as standard deviation and the derivative of a signal, two equations

which form the basis for calculating the Activity and Complexity of a signal. These calculations

were later verified through comparison with MATLAB that these were desirable results. These

results were identical to 10 decimal places, which should be more than adequate for the

purposes of this project.

Standard Deviation:

4-1

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Derivative of a Signal

4-2

4.3 Programs

Classifier Training Program (PC)

The first program created was a simple program which loaded a signal file and created a

instance of a Naïve-Bayes Classifier model, trained this model on the signal file and provided

group data (the same data used to train the MATLAB classifiers), and its performance was

evaluated by running it against another test set of data.

From the libraries provided by WEKA, a Naïve-Bayes linear classification algorithm was chosen

and a classifier model trained and evaluated on sample data through cross validation. The

resulting trained model can be serialized and saved to a .MODEL file. This allows for a classifier

model to be trained and evaluated offline before the model is ported to the target android

platform. This is important functionality because the training of a classifier is the most

processor intensive and most power-hungry part of developing classifier models. The Android

device simply does not have the required computational power to do this.

Two important methods were created from within the model class in java. One method

performed an evaluation of the classifiers performance. The other method was intended for

end use by the Android application and classified an individual instance of data against the

classifier model. Once the model was created and evaluated, this program serializes and saves

the trained java classifier object to a .MODEL file to the project directory. This .MODEL file can

then be ported to the Android device, where the java classifier can be de-serialized and used

for classification of received signals.

Porting Classifier to Android

Following the creation of a program which would run on PC, a simple Android program was

developed. This program opened and de-serialized the .MODEL file provided by the PC

program. The app then ran classification on hard-coded data in order to verify the

classification algorithm was functioning on the hardware.

Development of the classification program on the Android platform required that some

changes had to be made to the source WEKA libraries in order to make them compatible with

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the OS. Doing this required certain parts of the WEKA libraries to be stripped away [9].It

should be noted here that the java program used to generate the classifier model offline

should use an identical version of the WEKA library used by the Android OS. This is to avoid

issues which arise when serializing a .MODEL file and then attempting to de-serialize this

.model file on the target device, as they will not be compatible. In other words, both the

offline classification training program and the classification application running on the Android

target device must use identical jar libraries. It is not possible to serialize a .MODEL file with

one version of WEKA and de-serialize the file with another.

There was some time spent investigating if it was possible to implement the MRE (MATLAB

Runtime Environment) on the Android Platform. For a PC based OS, the MRE would allow

developers to create Java classes directly from the MATLAB IDE or command-line interface,

and implement them in Java programs provided the correct version of the MRE is present on

that system. This was abandoned in favor of a simpler approach. At this point, attention

shifted towards using the WEKA java libraries in order to implement a classifier on the Android

platform.

Bluetooth Client and Server Programs (Android and PC)

The third and fourth programs created throughout this project are Bluetooth client and server

programs which run on the Android Device and PC respectively. This Android program was

based off the Bluetooth Chat app provided by Google and a simple Bluetooth Chat to PC

application sourced online [8]. The java PC server was programmed through the BlueCove API

Libraries. The main aim was to merge the Client app with the classification program and allow

the Server to send EEG data over a Bluetooth link every 4 seconds to allow the mobile

platform to convert data close to real-time. Following the development of a Bluetooth Server-

Client connection between PC and Android, it was intended to create a connection between

the Emotiv EPOC Headset and PC. The full intention was to allow the PC to act as a ‘relay

device’ to allow the Emotiv EPOC headset to transmit data to the Android device in real time.

Figure 4-1 demonstrates this intention visually. The PC would also be used to buffer data from

the headset (sampling at 128Hz) into frames of 1024 samples to be transmitted to the Android

device every 8 seconds.

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EMOTIV EPOC PC ANDROID

Data

transmitted at

128Hz

Buffered at

frames of 1024

Figure 4-1: Flowchart showing high-level design

When determining the suitable system architecture for the real-time Android application, it

was decided that due to the time friendly nature of the original aim for the application (1

frame of data every 4/8 seconds) a simple control loop was determined to suffice, as shown in

Figure 4-2:

Read Data

from Buffer

Extract

Features

Classify

Data

Wait for

Buffer

Figure 4-2: Flowchart of Real-Time System Loop

This would require that the time taken for the total processing required for these 4 steps to be

under 8 seconds, allowing the system to return to a wait state before the next frame of data is

ready. Bluetooth was chosen due because it is commonplace in devices which are used in

short-distance communication and because it was readily available in the chosen hardware.

The development of Bluetooth Server for java involved the inclusion of BlueCove API

(mentioned in the chapter 2, ‘System Overview’) which allows for development of Bluetooth

(JSR-82 implementation) with Bluetooth enabled devices with the proper JRE version installed.

P a g e | 25

The Bluetooth Client side of the application was built using Bluetooth Chat example provided

by the Android SDK as a basis.

The Bluetooth Server was developed on top of an existing Bluetooth PC Server example

online. This server was modified to receive a connection from an Android client and begin

transmitting stored data from the Freiburg Database at rates specified within the code. To

transmit the data, each frame is parsed to a string with hard-coded delimiters separating each

sample. Object input and output streams are then opened between server and client and the

String is passed between programs at a fixed rate.

Seizure Application

The Android Application is made of 4 main classes. Each of these classes can be view in full on

the supplied CD. The main design aim was to create an application which would classify EEG

data in or close to real time. These results would be displayed on screen for programmer

verification. The code also logs all results to Android LogCat for verification.

RemoteBluetoothService.java

This Android service is created on Activity startup and remains running until stopped by

Activity or killed by Android OS. This service creates the Bluetooth Adapter, searches for

Bluetooth devices when called by DeviceListActvity, and connects to Bluetooth devices

specified by DeviceListActivity.

DeviceListActivity.java

This Activity creates a ListView widget overlay populated with Bluetooth device names and

ID’s retrieved from RemoteBluetoothService.java and allows the user to specify what

Bluetooth device they wish to connect to.

RemoteBluetooth.java

This is the main Activity. On startup, first checks to see if Bluetooth has been enabled on the

device. If not, a dialogue appears requesting user to turn on Bluetooth. On a ‘No’ click,

program shuts down. On ‘yes’ this activity starts RemoteBluetoothService.java. From this

Activity, DeviceListActivity.java can be called, and RemoteBluetoothService.java can be

stopped. RemoteBluetooth.java controls the main GUI interface for displaying current Activity,

Complexity and Ictal State to the user. This information is purely for immediate programmer

feedback, it is not expected for a Patient to need to monitor these values and creating a user-

friendly GUI for the application is outside the scope of this project. As mentioned earlier, the

P a g e | 26

aim is to validate the possibility of a mobile seizure detection system with the Smartphone as

the central processing hub.

WEKALDA.java

This java class contains the Classifier model and creates the Attributes and Elements which are

the WEKA libraries equivalent to Features and States. This class also includes a method for

classifying data against the stored Classifier Model.

Application Screenshots

Figure 4-3: Application Screen Captures; Application requesting Bluetooth [left], Scanning for devices [centre],

Classifying data [right]

Issues with HTC Wildfire:

Working with the HTC Wildfire as the Mobile Device of choice proved problematic throughout

for the beginning of the development of the Bluetooth application for Android. The issue

related to the device being able to see and pair with other Bluetooth devices, but unable to

connect. The problem was eventually traced back to the following line of code, the

workaround for which is featured in Appendices A.3. This issue halted development on the

application for around 3 weeks during the Christmas period.

3.5 Emotiv EPOC Development

Developing the system to incorporate real-time classification of EEG data required a number

of steps. Firstly, this required a direct connection between PC and the Emotiv EPOC over

Bluetooth had to be established, and values sensed from the Emotiv EPOC at a rate of 128Hz.

The next requirement of the system was to establish a method from which this signal could be

P a g e | 27

processed from within a java program. This presented a number of issues which will be

discussed later.

The simple C++ examples acquired from the Emotiv EPOC documentation were extended to

log EEG data to a CSV file which was stored on the desktop. The java server program could

then be run, which reads the CSV file from the desktop and stores the values in an array, and

buffers the array into blocks of 1024 samples. These samples were then transmitted over

Bluetooth connection to the Android device where classification could be carried out.

There were issues related to obtaining a true real-time connection between the Emotiv EPOC

Headset and the Android phone. The Emotiv EPOC is not Android compatible therefore it is

not possible to directly connect the Android Phone to the Emotiv EPOC through Bluetooth.

This led to the decision to use a PC as a relay point as mentioned earlier. However, this also

proved to be problematic. The main issue is the incompatibility between Java (Android OS)

and C++ (Emotiv EPOC).

Two methods of connecting the C++ and Android applications on PC were investigated,

including the Java Native Access (JNA) Wrapper provided with the system documentation and

a C++ to Java Socket Client-Server. The JNA Wrapper has been used successfully with projects

carried out on other systems [10] but proved ineffective here, likewise the Java-C++ Client-

Server

Emotiv EEG signals

The main difference between data gathered from Emotiv and data gathered from Freiburg

Database, is mentioned above, is that the Emotiv signal has undergone preprocessing.

Therefore EEG classification of Emotiv data against a model trained from the database is

unreliable. Due to Health and Safety limitations, Epileptic patients are inaccessible for seizure

data gathering utilizing the Emotiv EEG headset as the collection device. As such, it can only be

verified that data can be transmitted from Headset to Android and that classification does

occur.

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3.6 Results A java classifier model was created and performance evaluated using a small test set. This

model could be ported to an Android device for use in mobile classification. A working

Android Application which classifies EEG signals using basic features and a lightweight Naïve-

Bayes linear classifier was created. The Android App was tested to take buffered data at frame

sizes of 128 every half second.

Its impact on phone power consumption and the speed which it performs classification was

measured. It was intended to also focus time on developing digital filters in java, but this was

not completed within the project timeline.

Application Statistics

Contained in Figure 4-4 are the results of a WEKA Linear Classifier model, evaluated through

WEKA’s evaluateModel function

Figure 4-4: Results output of the Naïve-Bayes Classifier using the evaluateModel function from WEKA libraries

Using an application called PowerTutor designed by students in the University of Michigan

[11] the power consumption of the application while active was processing could be

estimated, graphed in Figure 4-5 below.

Sensitivity: 0.9711981566820277

Specificity: 0.03225806451612903

Correctly Classified Instances 844 93.8821 %

Incorrectly Classified Instances 55 6.1179 %

Kappa statistic 0.0037

Mean absolute error 0.0669

Root mean squared error 0.2448

Relative absolute error 99.0938 %

Root relative squared error 134.1465 %

Coverage of cases (0.95 level) 94.327 %

Mean rel. region size (0.95 level) 51.6129 %

Total Number of Instances 899

Total Number of Instances 899

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Figure 4-5: PowerTutor measurements of Seizure Detection App; Power usage over a period of 10 seconds [left]

and Power usage as a percentage of whole application [left]

The average runtime of the Classification algorithm is approximately 5-8ms per frame.

3.7 Conclusions The Emotiv EPOC Headset has numerous limitations which devalue its continued use within

the scope of future projects similar to this. The headset itself is large and bulky in design, and

is not suited for use while the user is mobile. Erratic movement cause major interference and

noise in signal output.

In order to continue exploring the use of mobile technology in detecting a seizure within an

environment where the users themselves are mobile, project specific hardware must be

constructed in order to provide more stability to the system and reduce the affect that

physical movement has on the quality of the EEG signal received. This will be discussed in

more detail later in Chapter 6.

However, completion of the java classifier program on Android suggests that it is indeed viable

to use a mobile device such as an Android phone as a portable platform for monitoring of EEG

state. Despite issues relating to the Emotiv EPOCs physical limitations, this report proves that

the Android device is able to handle a Bluetooth stream of data from an EEG source at

desirable frequencies and performance data classification. Connecting the Emotiv headset to

Android for live-data BAN simulation would have been ideal, but not a vital part of this

project.

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5. Compression of EEG Data

5.1 Introduction

This chapter deals with the small scale study conducted as part of this project into the effect

of compression of EEG data on classifier performance and its behavior. Compression of data

offers numerous benefits in systems like the one described throughout this paper, including

reducing saving space in memory and having potential power saving benefits. For this study,

the SPIHT algorithm was employed in MATLAB in order to investigate the effects of

compression. Due to the time sensitive nature and wide scope of this project, the SPIHT

algorithm is not developed or implemented in Java code. Instead, on supervisor advice the

algorithm will be treated as a ‘Black Box’ wherein only the effect on EEG data and the seizure

detection algorithms performance is studied, not the SPIHT algorithm itself.

5.2 Method of Study

For this small study, data was again sourced from the Freiburg Database. This data was passed

into a compression program in MATLAB. Detail on the SPIHT Algorithm can be found earlier in

Chapter 2: Project Components.

There are 2 main variants of the MATLAB SPIHT program code found on the CD provided with

this thesis. The first variant (driver1.m) takes a 1024 sample frame of data from an EEG signal

file and calculates the PRD (Percentage Root-Mean-Square Deviation) for 50 different CR

(Compression Ratio) values and plots these to a graph. The second variant (my_genSPIHT.m)

takes a full signal file, compresses the data to a specified CR and reconstructs the data for use

in evaluating change in classifier performance. Figure 5-1 is a visual representation of the

signal compressed at ratios of 1:4, 1:25, 1:50 and 1:100 and compared to the original signal.

For this section both Linear Discriminate Analysis and Support Vector Machine performance

was evaluated against the compressed datasets.

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Figure 5-1: Comparison of EEG Signal at varying CR

For the study 4 main areas of effect of compression on EEG and Classifier performance were

analyzed. The PRD is a measure of how accurate the reconstructed signal is when compared to

the original waveform. A higher PRD value demonstrates a higher level of distortion compared

to the uncompressed signal. The compression ratios were calculated programmatically as in

Figure 5-2:

Figure 5-2: Pseudo-Code for calculation of CR

CorrectRate, Sensitivity and Specificity are the values used earlier in this paper to evaluate the

performance of different classifiers.

NumberofPoints = 100

totalBits = length(bits)

fractionBitLen = nearestWhole((totalBits/numberOfPoints));

for i = 1 to NumberofPoints do{

reconstructedBits = fractionBitLen*NumberofPoints

x = doSPIHTCompression(signal, reconstructedBits)

x = decompressSignal(x)

}

end

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5.3 Results

Figure 5-3 demonstrates the increase in PRD as the CR increases incrementally for 100

different compression ratios.

Figure 5-3: Percentage Root-Mean-Squared Deviation vs. Compression Ratio using SPIHT Compression

Tables 5-1 and 5-2 each demonstrate the effect of different SPIHT Compression ratios on the

LDA and SVM Classifier performance estimation values respectively. The tables demonstrate

the average change in percentage performance of the LDA Classifier, and the second set is

representative of the Support Vector Machine Classifier.

Linear Discriminate Classifier:

Compression Ratio Sensitivity Specificity Correct Rate

4:1 -0% -0.28% -0.05%

25:1 -0.05% -7.94% -0.16%

50:1 -0.23% -10.32% -0.38%

100:1 -0.4% -12.7% -0.61%

Table 5-1: Average change in performance of LDA Classifier vs. CR

P a g e | 33

Support Vector Machine:

Compression Ratio Sensitivity Specificity Correct Rate

4:1 -0% -0. 79% -0.78%

25:1 -0 % -3.01 % -2.0%

50:1 -0.28% -4.84% -4.76%

100:1 -14.88% -2.645% -4.18%

Table 5-2: Average change in performance of SVM Classifier vs. CR

5.4 Conclusions

Graphing the above data shows a steady decline in Specificity in accordance with Compression

Ratio, thus demonstrating that the most significant impact of increasing Compression Ratio is

a decreasing ability in the algorithms ability to correctly detect negative results. Also, the SVM

classifier itself seems to show extra sensitivity towards the change in CR compared to the

Linear Classifier, which suggests that under circumstances where data compression is

involved, the Linear Classifier would be the more robust choice. However, the performance of

both classifiers on compressed EEG is relatively similar to that of the original signal, suggesting

that the system could benefit from data compression, without a significant drop in

performance.

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6. Discussion

6.1 Introduction This chapter aims to put in context what has been achieved through the timeline of this

project, areas which caused trouble, what was successful and what has still to be done in

terms of a project with this scope.

6.2 Discussion In chapter 3, the Algorithm Development phase of this project was covered. This phase heavily

depended on the use of MATLAB in developing the algorithm, being required for measuring

algorithm performance, extraction and comparison of features, examination of the effects and

benefits of signal pre-processing and ultimately deciding on an algorithm to move forward

with into the mobile development phase of the project. The resulting algorithm proved

effective on test data and fit the requirements outlined throughout the project.

In chapter 4, the development of the mobile system was discussed. The initial Android Seizure

Classifier Application was completed in full. This system connected over Bluetooth to a

Bluetooth Server containing EEG data. This data could be transmitted in frames to the Android

device, which would then perform classification of the received data.

This phase came with numerous difficulties, the two major issues being the development of

the Bluetooth connection between PC and the HTC Wildfire phone, and the development of a

real-time system relay between the Emotiv EPOC and Android phone through the PC. The

Bluetooth connection issue was specific to the HTC Wildfire and limited other HTC models.

The issue was not well documented and therefore a work-around was hard to find. This issue

could have been mitigated had the program been tested on a second device earlier in the

Bluetooth development cycle. This would have identified the issue as being hardware specific

at an earlier stage and a solution could have been resolved quicker.

The second issue faced at this stage related to the incompatibility between the Emotiv EPOC

headset and Android. This issue arose due to the incompatibility issues between Java and C++.

This was unforeseen to a degree, as the Emotiv headset documentation provides a JNA

Wrapper class for Java to allow Java applications to access Native DLL libraries on a system in

order for these programs to read EEG data from the Emotiv Headset. However after numerous

attempts, which involved changing configuration settings, changing system paths and project

class paths, this method was abandoned. However this method has seen success in other

projects, as referenced earlier in Chapter 4. The second attempted solution was also to modify

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an example C++/Java Client-Server code, but this also proved unsuccessful. The issue

regarding connecting the Emotiv headset directly or indirectly to Android is still open for

resolution.

As the project progressed, there are a number of limitations that have been noted with regard

to the Emotiv EPOC Headset as a mobile EEG Capture device, and its usefulness within this

project, as touched upon earlier in Chapter 4. Firstly, being restricted to the use of healthy

personal for live testing means it is very difficult to determine if the classifier is working on live

data. In order to continue development, Ictal data must be collected beforehand on the EEG

sensing device which is to be used in the mobile BAN system. The Emotiv EPOC is not suited to

this purpose due to its bulky nature. This causes additional movement when a patient is

mobile and increases the level of noise on the EEG signals.

Chapter 5 detailed the small scale study into the effects of compression of EEG signal and the

resulting impact compression has on Classifier performance. This small scale study identified

an inverse relationship between increasing CR and classifier specificity. However, the

percentage of correctly identified instances only dropped by roughly 4.5% perfect between

compression ratios of 50:1 to 100:1, suggesting that either the features used in classification

in this project, or the classification algorithms themselves are robust in terms of dealing with

compressed data.

With regards to the Project Plan outlined for Semester 1 and Semester 2 and the relative

effectiveness of both: the Project Plan for Semester 1 proved to be very successful and was

adhered to throughout the course of the Semester. The plan allowed ample time for research,

implementation and testing. The Semester II plan could have featured more detail and offer

more time for development considering the newer technologies involved (e.g. Bluetooth and

the Emotiv EPOC).

6.3 Conclusion The aim of this project was to investigate the development and implementation of a number

of signal processing algorithms for epileptic seizure detection through digital signal processing

of EEG signals, within the mobile digital health environment. It aimed to combine off-the-shelf

hardware for sensing EEG signals and Smartphone technology to act as the processing hub to

create a BAN for monitoring of EEG signals. The project also aimed to carry out small scale

investigation of compression of EEG signals in order to determine if there was a relationship

between signal compression and seizure detection performance. To this end, the project

achieved all of the milestones set out in the opening chapter, with the exception of measuring

P a g e | 36

the potential power savings to be gained from the use of data compression. A mobile

application was created which could process EEG signals and classify Ictal activity. This

application could be connected through Bluetooth to a compatible EEG source, essentially

demonstrating that wireless EEG signal processing through mobile technology was indeed

possible. The small-scale study into EEG compression was completed and demonstrated an

inverse relationship between signal compression and classifier specificity. However also

demonstrated was the off-the-shelf system used within this project proved to have many

limitations in a real-life application such as the one described in this project. Therefore it

should be deemed unsuitable for further use in projects such as this.

It is advised that future projects and studies into the area of mobile EEG monitoring should

focus on developing hardware specific solutions to the EEG sensing issues. Specific hardware

could be constructed which would be designed to remain physically stable during patient

mobility in order to significantly remove noise. EEG hardware could also be compatible with

mobile devices to give the system practical value.

This project opens up a new area of patient “wearable” systems for monitoring and analyzing

biological signals. This projects value could be increased as further research continues into

areas such as seizure prediction, which when combined with the research in this project, could

provide epileptic patients with “early warning” systems to minimize risk to self.

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References

[1]EEG Database,” Seizure Prediction in Freiburg, Germany - https://epilepsy.uni-

freiburg.de/freiburg-seizure-prediction-project/eeg-database

[2]WEKA: Data Mining Software in Java, University of Waikato

http://www.cs.waikato.ac.nz/ml/weka/

[3] MATLAB EEG code, The Andersen Lab. Division of Biology California Institute of

Technology Pasadena, CA 91125-http://www.vis.caltech.edu/~rodri/programs/spect.m

[4] Niina Päivinen, Seppo Lammi, Asla Pitkänen, Jari Nissinen, Markku Penttonen,

Tapio Grönfors, ”Epileptic seizure detection: A nonlinear viewpoint”, Computer Methods and

Programs in Biomedicine, Volume 79, Issue 2, August 2005, Pages 151-159.

doi:10.1016/j.cmpb.2005.04.006

[5]M. Vourkas, S. Micheloyannis and G. Papadourakis, ”Use of ANN and Hjorth

parameters in mental-task discrimination”, First International Conference on Advances in

Medical Signal and Information Processing

[6]Hamid R. Mohseni, A. Maghsoudi and Mohammad B. Shamsollahi, “Seizure

Detection in EEG signals: A Comparison of Different Approaches”, 28th International Annual

Conference IEEE Engineering in Medicine and Biology Society(EMBS), August 2006,

http://embc2006.njit.edu/pdf/1721_Mohseni.pdf

[7] O. Blanke, G. Lantz, M. Seeck, L. Spinelli, R. Grave de Peralta, G. Thut, T.

Landis, C.M. Michel, “Temporal and spatial determination of EEG-seizure onset in the

frequency domain”, Clinical Neurophysiology, Volume 111, Issue 5, 1 May 2000, Pages 763-

772

[8] T. Cecchin, R. Ranta, L. Koessler, O. Caspary, H. Vespignani, L. Maillard, “Seizure

lateralization in scalp EEG using Hjorth parameters”, Clinical Neurophysiology, Volume 121,

Issue 3, March 2010, Pages 290-300

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*9+RJ Marson, “Weka for Android”, http://rjmarsan.com/#/Research/WekaforAndroid/

[10] A. Hoffman, “EEG Signal Processing and Emotiv’s Neuro Headset”, 28-09-2010,

Page 46

[11]PowerTutor, Mark Gordon, Lide Zhang, University of Michigan, http://ziyang.eecs.umich.edu/projects/powertutor/

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Appendix

A.1 HTC Wildfire Specifications:

Relevant Specs:

OS Android OS, v2.3 (Gingerbread), upgradable

to v2.3.5

Chipset Qulacomm MSM7227

GPU 600 MHz ARM 11

CPU Adreno 200

Java Yes, via Java MIDP emulator

Battery Standard battery, Li-Ion 1230 mAh

Bluetooth Yes, v3.0

Table A-1: Specifications of HTC Wildfire.

A.2 MATLAB Filter Code

Figure A-1: Code used to design MATLAB Filter Coefficients

function hd = filterB() %FILTERB Summary of this function goes here % Detailed explanation goes here

%sampling frequency fsamp = 256; bandstart=2; bandstop=20; N=50;

W1 = (bandstart/(fsamp/2));

W2 = (bandstop/(fsamp/2));

W = [W1 W2];

hd = fir1(N, W); end

P a g e | 40

A.3 Wildfire Bluetooth Code Problem

In RemoteBluetoothService.java, ConnectThread method, a Service Discovery Failed Exception

occurs. This error seems to stem from HTC lacking Bluetooth support in some phone models.

The code below was provided as a work-around.

Offending Code:

tmp = device.createRfcommSocketToServiceRecord(MY_UUID);

Replaced With:

Method m = device.getClass().getMethod("createRfcommSocket", new

Class[] {int.class});

tmp = (BluetoothSocket) m.invoke(device, 1);