1. Support Feature Machine for Classification of Abnormal Brain Activity
W. Art Chaovalitwongse
Rutgers University
*Joint work with Y.J. Fan (Rutgers) and R.C. Sachdeo (Jersey Shore University Hospital)
Poster #16, Mon Aug 13
This work is supported in part by research grants from NSF CAREER Grant CCF and Rutgers Research Council Grant

2. Agenda Support Feature Machine Empirical Results Research Objectives
Research Background
Epilepsy and seizures
Electroencephalogram (EEG) time series
Signal processing
Support Feature Machine
Empirical Results
Concluding Remarks

3. Objectives:
Develop a new pattern recognition and classification framework for multi-dimensional time series data – as a decision making support.
Application in Epilepsy
Identification of Seizure Pre-Cursor: Classification of seizure susceptibility periods
Quick Screening Tool: Classification of epilepsy and non-epilepsy patients
Seizure Prediction: Anomaly (Seizure Pre-Cursor) detection
Generalize the framework to other applications that have to deal with multi-dimensional time series data.

4. How many people having epilepsy?
“The Swamp”: Seating capacity ~90,000

5. Epilepsy and Seizures
Nearly 3 million people in the U.S. (1% of population) have epilepsy. Anyone, at any age, can develop it.
Epilepsy is defined as recurring seizures – sudden, brief changes in the way the brain works.
Seizures cause temporary disturbances of brain functions such as motor control, responsiveness and recall which typically last from seconds to a few minutes.
Seizures usually occur spontaneously, in the absence of external triggers.
Seizures may be followed by a post-ictal period of confusion or impaired sensorial that can persist for several hours.

6. Intracranial EEG Acquisition

7. Electroencephalogram (EEG)
…is a traditional tool for evaluating the physiological state of the brain.
…offers good spatial and excellent temporal resolution to characterize rapidly changing electrical activity of brain activation
…captures voltage potentials produced by brain cells while communicating.
In an EEG, electrodes are implanted in deep brain or placed on the scalp over multiple areas of the brain to detect and record patterns of electrical activity and check for abnormalities.

8. 10-second EEGs: Seizure Evolution
Normal
Pre-Seizure
Seizure Onset
Post-Seizure

9. Open Problems Seizure pre-cursors exist?
Seizure is a state transition process?
Can we discriminate normal EEGs from pre-seizure EEGs (seizure susceptibility period)?

10. Data Transformation Using Chaos Theory
Measure the brain dynamics from time series:
Stock Market
Currency Exchanges (e.g., Swedish Kroner)
Apply dynamical measures (based on chaos theory) to non-overlapping EEG epochs of seconds = 2048 points.
Maximum Short-Term Lyapunov Exponent
measure the average uncertainty along the local eigenvectors and phase differences of an attractor in the phase space
measure the stability/chaoticity of EEG signals
Iasemidis, Shiau, Chaovalitwongse, Sackellares & Pardalos, IEEE Transactions on Biomed (2003)

11. Measure of Chaos

12. Classification of Physiological States

13. Support Vector Machine VS Support Feature Machine

14. Nearest Neighbor for Time Series
Normal
Pre-Seizure A
d1: Average distance to blues.
d2: Average distance to reds.
d2 < d1, so new point is classified as red.

15. Similarity Measures Dynamic Time Warping (DTW) Distance
Euclidean Distance
T-Statistical Distance
STLmax
1, 2, 3, … , 30
STLmax
1, 2, 3, … , 30
Electrode 1 2 3 . 26
D(X, Y) Y X

16. Support Feature Machine
Given an unknown epoch of EEG signals A, we calculate statistical distances between the EEG epoch and the groups of Normal and Pre-Seizure EEGs in our data baseline.
Euclidean distance
T-statistical distance
Dynamic Time Warping
EEG sample A will be classified in the group of patient’s state (normal or pre-seizure) that yields the minimum statistical distance.
Multiple Electrodes = Multiple Decisions
Averaging
Majority Voting: selects action with maximum number of votes
Can we select/optimize the selection of a subset of electrodes that maximizes number of correctly classified samples.
Chaovalitwongse et al., Submitted to Operations Research

17. Decision Rule: Basic Ideas
Two different average distances for each sample at each electrode are calculated:
Intra-Class: Average distances from each sample to all other samples in the same class at Electrode j
Inter-Class: Average distances from each sample to all other samples in different class at Electrode j
If for Sample i at Electrode j (Averaging VS Voting)
Average distance to the same class
<
Average distance to different class
Then Sample i is correctly classified.

18. Optimization Model I: Averaging
Intra-Class
Inter-Class
Chaovalitwongse et al., Submitted to Operations Research

19. Model I: Averaging Formulation
Chaovalitwongse et al., Submitted to KDD, 2007 and Operations Research

20. Optimization Model II: Voting
Precision matrix, A contains elements of
Chaovalitwongse et al., Submitted to Operations Research

21. Decision Rule: Basic Ideas
Two different average distances for each sample at each electrode are calculated:
Intra-Class: Average distances from each sample to all other samples in the same class at Electrode j
Inter-Class: Average distances from each sample to all other samples in different class at Electrode j
If for Sample i at Electrode j (Averaging VS Voting)
Average distance to the same class
<
Average distance to different class
Then Sample i at Electrode j is correctly classified.
Chaovalitwongse et al., Submitted to Operations Research

22. Model II: Voting Formulation
Chaovalitwongse et al., Submitted to KDD, 2007 and Operations Research

23. Data Selection and Sampling
EEG Dataset Characteristics
Patient ID
Seizure types
Duration of EEG(days)
# of seizures 1
CP, SC
3.55 7 2
CP, GTC, SC
10.93 3 CP
8.85 22 4
,SC
5.93 19 5
13.13 17 6
11.95
3.11 9 8
6.09 23
11.53 20 10
9.65 12
Total
84.71
153
CP: Complex Partial; SC subclinical; GTC: Generalized Tonic/Clonic
Randomly and uniformly sample 3 EEG epochs per seizure from each of normal and pre-seizure states.
For example, Patient 1 has 7 seizures.
There are 21 normal and 21 pre-seizure EEG epochs sampled.
Seizure
Duration of EEG
30 minutes
8 hours
Pre-seizure
Normal

24. Sensitivity and Specificity
Sensitivity measures the fraction of positive cases that are classified as positive.
Specificity measures the fraction of negative cases classified as negative.
Sensitivity = TP/(TP+FN)
Specificity = TN/(TN+FP)
Sensitivity can be considered as a detection (prediction or classification) rate that one wants to maximize.
False positive rate can be considered as 1-Specificity which one wants to minimize.

25. 5-Fold Cross Validation Result
81.29% %
Optimize the number of neighbors
Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A, 2008
Chaovalitwongse et al., Submitted to Operations Research

26. DTW
Euclidean
T-Statistics

27. DTW
Euclidean
T-Statistics

28. Automated Seizure Prediction Paradigm
Multichannel
Com
Feature Extraction/ Cluster Analysis
Data Acquisition
Interface
Technology
Pattern
Recognition
VNS
Initiate a variety of
therapies (e.g., electrical stimulation, drug injection)
User
Drug

29. Concluding Remarks
Overview of a Real Life Medical Problem in Spatio-Temporal Data Mining
Applications of Data Mining and Optimization Techniques
Potential Applications in Medical Diagnosis
Automated seizure warning system
Monitoring devices for clinical use in epilepsy monitoring units (EMUs) and intensive care units (ICUs)
Other monitoring procedures in trauma and operation rooms
Improvement of the Nearest Neighbor Classification in Time Series Classification - New Classification Framework

30. Reference
W. Chaovalitwongse, Y.J. Fan, R.C. Sachdeo. Novel Optimization Models for Multidimensional Time Series Classification: Application to the Identification of Abnormal Brain Activity. Submitted to Operations Research.
Y.J. Fan, W. Chaovalitwongse, L.D. Iasemidis, R.C. Sachdeo. Multi-Dimensional Time Series Classification for Identification of Epilepsy Patients. Submitted to KDD 2007.
W. Chaovalitwongse, Y.J. Fan, and R.C. Sachdeo. On the K-Nearest Dynamic Time Warping Neighbor for Abnormal Brain Activity Classification. To appear in IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2008.
W. Chaovalitwongse and P.M. Pardalos. On the Time Series Support Vector Machine using Dynamic Time Warping Kernel for Brain Activity Classification. To appear in Cybernetics and Systems Analysis, 2007
W. Chaovalitwongse, P.M. Pardalos, and O.A. Prokopyev. Electroencephalogram (EEG) Time Series Classification: Applications in Epilepsy. Annals of Operations Research, 148: , 2006.
W. Chaovalitwongse, L.D. Iasemidis, P.M. Pardalos, P.R. Carney, D.-S. Shiau, and J.C. Sackellares. A Robust Method for Studying the Dynamics of the Intracranial EEG: Application to Epilepsy. Epilepsy Research, 64, , 2005.
W. Chaovalitwongse , P.M. Pardalos, L.D. Iasemidis, D.-S. Shiau, and J.C. Sackellares. Dynamical Approaches and Multi-Quadratic Integer Programming for Seizure Prediction. Optimization Methods and Software, 20 (2-3): ,

31. Acknowledgements
Comprehensive Epilepsy Center, St. Peter’s University Hospital
Rajesh C. Sachdeo, MD
Rutgers Ph.D. Student
Ya-Ju Fan, MS
Industrial and Systems Engineering, University of Florida
Panos M. Pardalos, PhD
Bioengineering, Arizona State University
Leonidas D. Iasemidis, PhD