Transcript
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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10 20 30 40 50 60 70 80 90 100 110 120-2
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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 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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(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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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
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+ –
+
–
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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0 5 10 15 20-200
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Voltage label ( v)
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Empirical pdf
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Voltage label ( v)
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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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