MS Thesis Defense

Detection of Epileptic Seizures using Statistical Features in the EMD Domain

S M Shafiul AlamStudent No: 0409062207

Department of Electrical and Electronic EngineeringBangladesh University of Engineering and Technology

Outline Background Motivation Objective Methodology Results and Discussion Conclusion Future Research Questions and Answer

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BACKGROUND

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Human Brain

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Complex network of approx. 100 billion neurons

Information processing Communication with and within rest of the

body

Human Brain

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Neuron – fundamental element of the network Interconnected using Axon and Dendrite Transmission of electric impulse

Synapse

Epilepsy and Seizure

Most common and serious neural disease Results from brain malfunction Causes recurrent seizure activity

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Epilepsy and Seizure

Violent body shacking Muscle twitching Lost of consciousness

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Unpredictable Unexpected injury Death

EEG Electroencephalogram Records neural activity from electric impulses Non-stationary Non-linear

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Scalpelectrode

Subduralelectrode

Depthelectrode

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Normal Pre-Seizure

SeizurePost-Seizure

Detection of Seizure

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Continuous Monitoring

How long is the EEG records? How many patients are there? Hospitalization for how long?

Automatic System

Alternative to continuous monitoring

Gives alarm when seizure occurs Enables automated drug delivery Less hospitalization

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MOTIVATION

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Automatic EEG Monitoring System

Diagnosis Is the person suffering from epilepsy?

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Detection Is the person experiencing a seizure?

General Strategy

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EEG records

Pre-processing

Feature extraction

Feature based classification

Pre-processingMandatory Noise suppression Artifact reduction (e.g. muscle activity, heart activity, eye activity)

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OptionalTransform into another domain (e.g. FT, DWT, TFA etc.) Power spectrum estimation

Feature extraction

Energy content Statistical feature (e.g., mean, standard deviation, variance etc.) Chaotic feature (e.g. LE, CD, FD, AE etc.)

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Feature based classification

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Threshold based decisionLinear classifierNaïve BayesKNNANNSVM

Issues

Classification performance Computational requirement Speed Practical implementation

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OBJECTIVE

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Transform EEGs into EMD domain Extract EMD domain statistical and chaotic

features Investigate features’ discriminating

capability Design ANN based classification system Study performance for epilepsy diagnosis

and seizure detection

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EMD Empirical Mode Decomposition Output – set of IMFs Independent of linearity/non-linearity Independent of stationarity Adaptive Requires no basis function (Different from FT, DWT and other

TFA)21

Sifting Process

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2IMF 1; iteration 0

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Local max. ()

Sifting Process

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10 20 30 40 50 60 70 80 90 100 110 120-2

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Envelope (max.)

Local min. ()

Envelope (max.)

Sifting Process

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10 20 30 40 50 60 70 80 90 100 110 120-2

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Envelope (max.)

Envelope (min.)

Mean

Sifting Process

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10 20 30 40 50 60 70 80 90 100 110 120-2

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residue

Residue

To be an IMFNo. of maxima and minima = No. of zero crossings

orNo. of maxima and minima – No. of zero crossings = ±1

METHODOLOGY

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EEG Online Database

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http://epileptologie-bonn.de/cms Five data sets One hundred EEG records in each set Sampling frequency: 173.61 samples/sec Length of each record: 4097 samples Available with artifacts omitted

Healthy group Five healthy persons(awaken and relaxed) A:- eyes opened B:- eyes closed

Epileptic group Five patients C:-Inter-ictal (hippocampal formation) D:-Inter-ictal (epileptogenic zone) E:- Ictal (epileptogenic zone)

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EEG samples

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Set B

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Set C

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Set A

Feature Extraction Procedure

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EEG

Low pass filter 9 Level EMD

Stat. feature

Chaotic feature

Stat. feature

Chaotic feature

Segmentation

Segmentation

Classification Procedure

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Stat. Feature

Chaotic Feature Classifier

Classifier

Stat. Feature

Statistical Features

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Voltage (v)

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Voltage (v)

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Voltage (v)

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(a) (b) (c)

Statistical Features

Average level of dispersion:- Variance Average level of asymmetry:- Skewness Average level of peakedness:-Kurtosis

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Statistical Features (Observation)

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Preliminary observation

Quite large variance for ictal EEGs compared to non-ictal ones

Rapid decrease of variance from IMF1 to IMF9 In terms of variance, skewness and kurtosis – larger

difference among five sets in EMD domain

One-way ANOVA (99% confidence interval)

Variance:- Band-limited EEGs and all IMFs Skewness:- Band-limited EEGs and first two IMFs Kurtosis:- Band-limited EEGs and first four IMFs

Chaos

Apparent disorder of a non-linear system Highly sensitive to initial condition Phase-space plot demonstrates chaotic

behavior (if any) of time-series data.

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Phase – Space Portrait

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Phase Space Plot of Healthy EEG

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Phase – Space Portrait

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Phase Space Plot of Inter-ictal EEG

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Phase – Space Portrait

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Phase Space Plot of Ictal EEG

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Chaotic Features

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Chaoticity:- Largest Lyapunov Exponent (LLE)

Complexity:- Correlation Dimension (CD)

Level of disorder:- Approximate Entropy (ApEn)

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Chaotic Features (Observation)Preliminary observation

No specific trend of LLE and CD values for Band-limited EEGs

Gradual decrease of LLE values from Set A to E for IMF1 and IMF2

Gradual decrease of CD values from Set A to E for IMF1 – IMF4

Gradual decrease of ApEn values from Set A to E for Band-limited EEGs, IMF1 and IMF2

For all IMFs, mean_LLE (Set A) > mean_LLE (Set E)mean_CD (Set A) > mean_CD (Set E)mean_ApEn (Set A) > mean_ApEn (Set E)

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One-way ANOVA (99% confidence interval)

LLE:- Band-limited EEGs and first four IMFs CD:- First four IMFs ApEn:- Band-limited EEGs and first five IMFs

Chaotic Features (Observation)

ANN-based Classifier

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Two-layer feed-forward network Hyperbolic tangent sigmoid transfer function Back-propagation training

Problem Formulation

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Input Feature

SetClassifier

Healthy

Ictal Epilepsy Diagnosis

Input Feature

SetClassifier

Ictal

Inter-ictal

Seizure Detection

Problem Formulation

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Input Feature

SetClassifier

Ictal

Inter-ictal

Detection and Diagnosis

Healthy

Diagnosis A, E

Detection D, E

Detection and Diagnosis A, D, E

Others (A,B,C,D), E (A,B), (C,D), E

RESULTS&

DISCUSSION

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Classification Performance

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Target ClassO

utpu

t Cla

ss

+ –

+

–

TP

TNFN

FP

Confusion Matrix

)/()()/()/(

FPTNFNTPTNTPAccuracyFPTNTNySpecificitFNTPTPySensitivit

Performance (3 Stat. Features)Detection (D, E) Better performance in EMD domain 100% accuracy for IMF3 and IMF4Diagnosis (A, E) Better performance in EMD domain 100% accuracy for IMF2 and IMF3Detection and diagnosis (A, D, E) Better performance in EMD domain 100% accuracy for IMF3

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Performance (3 Stat. +3 Chaotic Features)

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Detection (D, E) Better performance in EMD domain 100% accuracy for IMF3 and IMF4 Maximum accuracy reported to date:98.74% (FT, ApEn, AR modeling)Diagnosis (A, E) Better performance in EMD domain 100% accuracy for IMF2 and IMF3 Reported same performance using TFADetection and diagnosis (A, D, E) Better performance in EMD domain 100% accuracy for IMF3 Reported 99.28% accuracy using TFA

Stat. features – Maximum accuracy

Addition of chaotic features – similar performance

EMD domain statistical features play major role in characterizing EEGs

Explore the use of statistical modeling

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Statistical Modeling

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Sample data

Empirical pdf

Fitting with standard pdf

Estimated parameter

NIG and Stable pdfMoment based estimationEstimation requires large samples

Goodness-of-fit :- KLD, KS statistics

Statistical Modeling

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Empirical pdf

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Histogram plot

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Statistical Modeling

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1P-P Plot

Probability

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EmpiricalStable FitNIG Fit

Performance – Stat. Modeling

Band-limited EEGs – better fitting than IMFs

Stable pdf – better than NIG pdf

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CONCLUSION

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EMD domain statistics – better for detection and diagnosis

Less computational requirement Faster Less number of features ANN – optimum classifier Statistical model – promising method

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FUTURERESEARCH

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Extensive analysis using Stable pdf Maximum likelihood estimation Classification of visually inseparable EEGs Neo-natal seizure detection Sleep apnea detection Hypnosis detection Brain-computer-interfacing (BCI)

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THANKYOU

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QUESTIONS&

ANSWER

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