1. Medical Diagnosis Decision-Support System: Optimizing Pattern Recognition of Medical Data
W. Art Chaovalitwongse
Industrial & Systems Engineering
Rutgers University
Center for Discrete Mathematics & Theoretical Computer Science (DIMACS)
Center for Advanced Infrastructure & Transportation (CAIT)
Center for Supply Chain Management, Rutgers Business School
This work is supported in part by research grants from NSF CAREER CCF , and Rutgers Computing Coordination Council (CCC).

2. Outline Introduction Pattern-Based Classification Framework
Classification: Model-Based versus Pattern-Based
Medical Diagnosis
Pattern-Based Classification Framework
Application in Epilepsy
Seizure (Event) Prediction
Identify epilepsy and non-epilepsy patients
Application in Other Diagnosis Data
Conclusion and Envisioned Outcome
Here is the outline of this talk.
The focus of this talk will be on epilepsy and brain disorders
First I will try to convince the audience why this problem is important and those patients need our help
Then I will identify the research goals, then I’ll talk about how to acquire and process the data from the brain – specifically try to predict seizures
The second research challenge is how to use optimization and data mining techniques to recognize/or classify normal and abnormal brain data
– this framework can be applied to other medical data or data in other real life problems.

3. Pattern Recognition: Classification
Supervised learning: A class (category) label for each pattern in the training set is provided.
Positive Class ?
Negative Class

4. Model-Based Classification
Linear Discriminant Function
Support Vector Machines
Neural Networks

5. Support Vector Machine
A and B are data matrices of normal and pre-seizure, respectively
e is the vector of ones
 is a vector of real numbers
 is a scalar
u, v are the misclassification errors
m = number of samples for class 1
n = number of samples for class 2
Bradley, Fung and Mangasarian revamped this idea – using this robust optimization model – it is very fast and scalable
Mangasarian, Operations Research (1965); Bradley et al., INFORMS J. of Computing (1999)

6. Pattern-Based Classification: Nearest Neighbor Classifiers
Basic idea:
If it walks like a duck, quacks like a duck, then it’s probably a duck
Training Records
Test Record
Compute Distance
Choose k of the “nearest” records

7. Traditional Nearest Neighbor
K-nearest neighbors of a record x are data points that have the k smallest distance to x

8. Drawbacks Feature Selection Unbalanced Data
Sensitive to noisy features
Optimizing feature selection
n features, 2n combinations  combinatorial optimization
Unbalanced Data
Biased toward the class (category) with larger samples
Distance weighted nearest neighbors
Pick the k nearest neighbors from each class (category) to the training sample and compare the average distances.

9. Multidimensional Time Series Classification in Medical Data
Positive versus Negative
Responsive versus Unresponsive
Multidimensional Time Series Classification
Multisensor medical signals (e.g., EEG, ECG, EMG)
Multivariate is ideal but computationally impossible
It is very common that physicians always use baseline data as a reference for diagnosis
The use of baseline data - naturally lends itself to nearest neighbor classification
For multidimensional time series, it is ideal to do multivariate analysis – but it is computationally impossible in our application
In our work , we use univariate analysis – perform classification on each electrode at a time.
Then we use the idea of ensemble classification to make the final decision.
Normal ?
Abnormal

10. Ensemble Classification for Multidimensional time series data
Use each electrode as a base classifier
Each base classifier makes its own decision
Multiple decision makers - How to combine them?
Voting the final decision
Averaging the prediction score
Suppose there are 25 base classifiers
Each classifier has error rate,  = 0.35
Assume classifiers are independent
Probability that the ensemble classifier makes a wrong prediction (voting):
Most ensemble deal with how to sample the data Bagging, Bootstrapping Boosting, - here we use the idea of voting and averaging/or accumulating prediction score
Here I give an example why we use ensemble classification

11. Modified K-Nearest Neighbor for MDTS
Abnormal
Normal
K = 3
D(X,Y)
Time series distances: (1) Euclidean, (2) T-Statistical, (3) Dynamic Time Warping

12. Dynamic Time Warping (DTW)
The minimum-distance warp path is the optimal alignment of two time series, where the distance of a warp path W is:
is the Euclidean distance of warp path W.
is the distance between the two data point indices
(from Li and Lj) in the kth element of the warp path.
Dynamic Programming:
The optimal warping distance is
Exponential number of warp paths – we need to put some constraint on the warp path.
Figure B) Is from Keogh and Pazzani, SDM (2001)

13. Optimizing Pattern Recognition

14. Support Feature Machine
Given an unlabeled sample A, we calculate average statistical distances of A↔Normal and A↔Abnormal samples in baseline (training) dataset per electrode (channel).
Statistical distances: Euclidean, T-statistics, Dynamic Time Warping
Combining all electrodes, A will be classified to the group (normal or abnormal) that yields
the minimum average statistical distance; or
the maximum number of votes
Can we select/optimize the selection of a subset of electrodes that maximizes number of correctly classified samples

15. SFM: Averaging and Voting
Two distances for each sample at each electrode are calculated:
Intra-Class: Average distance from each sample to all other samples in the same class at Electrode j
Inter-Class: Average distance from each sample to all other samples in different class at Electrode j
Averaging: If for Sample i (on average of selected electrodes)
Average intra-class distance over all electrodes
Average inter-class distance over all electrodes
<
We claim that Sample i is correctly classified.
Voting: If for Sample i at Electrode j (vote)
Intra-class distance < Inter-class distance (good vote)
Based on selected electrodes, if # of good votes > # of bad votes, then Sample i is correctly classified.
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

16. Distance Averaging: Training
Sample i at Feature 1
∙∙∙
Sample i at Feature 2
Sample i at Feature m
Select a subset of features ( ) such that
as many samples as possible.
Industrial & Systems Engineering Rutgers University

17. Majority Voting: Training
(Correct) if ; (Incorrect) otherwise.
Negative
Positive i
Feature j i’
Industrial & Systems Engineering Rutgers University

18. SFM Optimization Model
Intra-Class
Inter-Class
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

19. Averaging SFM Maximize the number of correctly classified samples
Logical constraints on intra-class and inter-class distances if a sample is correctly classified
Must select at least one electrode
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

20. Voting SFM Precision matrix, A contains elements of
Maximize the number of correctly classified samples
Logical constraints: Must win the voting if a sample is correctly classified
Must select at least one electrode
Precision matrix, A contains elements of
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

21. Support Feature Machine

22. Support Vector Machine
Feature 3
Pre-Seizure
Feature 2
Normal
Feature 1

23. Application in Epilepsy Diagnosis

24. Facts about Epilepsy
About 3 million Americans and other 60 million people worldwide (about 1% of population) suffer from Epilepsy.
Epilepsy is the second most common brain disorder (after stroke), which causes recurrent seizures (not vice versa).
Seizures usually occur spontaneously, in the absence of external triggers.
Epileptic seizures occur when a massive group of neurons in the cerebral cortex suddenly begin to discharge in a highly organized rhythmic pattern.
Seizures cause temporary disturbances of brain functions such as motor control, responsiveness and recall which typically last from seconds to a few minutes.
Based on 1995 estimates, epilepsy imposes an annual economic burden of $12.5 billion* in the U.S. in associated health care costs and losses in employment, wages, and productivity.
Cost per patient ranged from $4,272 for persons** with remission after initial diagnosis and treatment to $138,602 for persons** with intractable and frequent seizures.
Today about 3 million americans and other 60 million people worldwide have epilepsy.
Epilepsy is the second most common brain disorder after stroke. It causes recurrent seizures, which appear to occur spontaneously and randomly.
What happens when someone has a seizure – in his or her brain, there is a massive group of neurons hypersynchronized in a highly organized rhythmic patterns – which lasts about 20 seconds to a few mins
This brain disease causes our country so much money – in 1995 estimate, it imposes an economic burden of $12.5 billions – no just healthcare cost - including job loss, productivity
Per patient, the healthcare cost ranged from 4k to almost 140k per year – and these numbers are more than 10 years ago.
By now I hope I’ve convinced the audience that we should do something about this disease – next I will discuss standard diagnosis, treatment, (acquired data) and how we can help these patients.
*Begley et al., Epilepsia (2000); **Begley et al., Epilepsia (1994).

25. Simplified EEG System and Intracranial Electrode Montage
Electroencephalogram (EEG) is a traditional tool for evaluating the physiological state of the brain by measuring voltage potentials produced by brain cells while communicating

26. Scalp EEG Acquisition
18 Bipolar Channels

27. Goals: How can we help? Seizure Prediction
Recognizing (data-mining) abnormality patterns in EEG signals preceding seizures
Normal versus Pre-Seizure
Alert when pre-seizure samples are detected (online classification)
e.g., statistical process control in production system, attack alerts from sensor data, stock market analysis
EEG Classification: Routine EEG Check
Quickly identify if the patients have epilepsy
Epilepsy versus Non-Epilepsy
Many causes of seizures: Convulsive or other seizure-like activity can be non-epileptic in origin, and observed in many other medical conditions. These non-epileptic seizures can be hard to differentiate and may lead to misdiagnosis.
e.g., medical check-up, normal and abnormal samples
Given multi-dimensional time series and a set of events/episodes (if you will). How can we predict the event
Classification of medical data (normal and abnormal) for guiding the future diagnosis
Feature selection -> initiating events – most differentiable

28. Normal versus Pre-Seizure

29. 10-second EEGs: Seizure Evolution
Normal
Pre-Seizure
Seizure Onset
Post-Seizure
Chaovalitwongse et al., Annals of Operations Research (2006)

30. Normal versus Pre-Seizure Data Set
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

31. Sampling Procedure
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.
Use leave-one(seizure)-out cross validation to perform training and testing.
Seizure
Duration of EEG
30 minutes
8 hours
Pre-seizure
Normal

32. Information/Feature Extraction from EEG Signals
Measure the brain dynamics from EEG signals
Apply dynamical measures (based on chaos theory) to non-overlapping EEG epochs of seconds = 2048 points.
Maximum Short-Term Lyapunov Exponent
measure the stability/chaoticity of EEG signals
measure the average uncertainty along the local eigenvectors and phase differences of an attractor in the phase space
Pardalos, Chaovalitwongse, et al., Math Programming (2004)

33. Evaluation Sensitivity = TP/(TP+FN) Specificity = TN/(TN+FP)
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)
Type I error = 1-Specificity
Type II error = 1-Sensitivity
Chaovalitwongse et al., Epilepsy Research (2005)

34. Leave-One-Seizure-Out Cross ValidationP1 N1
Training Set
SFM
Selected Electrodes N2 P2 1 2 3 4 5 6 7 .
2324 25 26 N3 P3 N4 P4
Testing Set N5 P5
N – EEGs from Normal State
P – EEGs from Pre-Seizure State
assume there are 5 seizures in the recordings

35. EEG Classification
Support Vector Machine [Chaovalitwongse et al., Annals of OR (2006)]
Project time series data in a high dimensional (feature) space
Generate a hyperplane that separates two groups of data – minimizing the errors
Ensemble K-Nearest Neighbor [Chaovalitwongse et al., IEEE SMC: Part A (2007)]
Use each electrode as a base classifier
Apply the NN rule using statistical time series distances and optimize the value of “k” in the training
Voting and Averaging
Support Feature Machine [Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)]
Apply the NN rule to the entire baseline data
Optimize by selecting the best group of classifiers (electrodes/features)
Voting: Optimizes the ensemble classification
Averaging: Uses the concept of inter-class and intra-class distances (or prediction scores)
First we implement a modified support vector machine, which is one of the most commonly used classification technique. The main idea is to

37. Performance Characteristics: Upper Bound
NN -> Chaovalitwongse et al., Annals of Operations Research (2006)
SFM -> Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)
KNN -> Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A (2007)

38. Separation of Normal and Pre-Seizure EEGs
From 3 electrodes selected by SFM
From 3 electrodes not selected by SFM

39. Performance Characteristics: Validation
Overfitting the data
Sample size
CPU time
SVM-> Chaovalitwongse et al., Annals of Operations Research (2006)
SFM -> Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)
KNN -> Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A (2007) 39

40. Epilepsy versus Non-Epilepsy

41. Epilepsy versus Non-Epilepsy Data Set
Routine EEG check: minutes of recordings ~ with scalp electrodes
Each sample is 5-minute EEG epoch (30 points of STLmax values).
Each sample is in the form of 18 electrodes X 30 points

42. Leave-One-Patient-Out Cross Validation
Training Set E1 N2 N3 N4 N5 E2 E3 E4 E5
SFM
Selected Electrodes 1 2 3 4 5 6 7 .
2324 25 26
Testing Set
N – Non-Epilepsy
P – Epilepsy

43. Voting SFM: Validation

44. Averaging SFM: Validation

45. 1 Fp1 – C3
T6 – Oz
Fz – Oz
The issue is not just to get 100% classification – rather we focus more on why we get that kind of results and understand the data.
For example, we look at the selected electrodes that help in distinguishing epilepsy and non-epilepsy patients.
We found 3 electrodes that play a major role – when we went back to the neurologists and talked to him. He was very surprised to see.
One would not expect to see that the selected electrodes would be involved in epilepsy mechanisms.
Again it could be the scalp electrode – one focus on the left but electrodes on the right pick up first.

46. Other Medical Diagnosis

47. Other Medical Datasets
Breast Cancer
Features of Cell Nuclei (Radius, perimeter, smoothness, etc.)
Malignant or Benign Tumors
Diabetes
Patient Records (Age, body mass index, blood pressure, etc.)
Diabetic or Not
Heart Disease
General Patient Info, Symptoms (e.g., chest pain), Blood Tests
Identify Presence of Heart Disease
Liver Disorders
Features of Blood Tests
Detect the Presence of Liver Disorders from Excessive Alcohol Consumption

48. Performance Training Testing LP SVM NLP SVM V-SFM A-SFM WDBC 98.08
96.17
97.28
97.42 HD
85.06
84.66
86.48
86.92
PID
77.66
77.51
75.01
77.96
BLD
65.71
57.97
63.46
66.43
LP SVM
NLP SVM
V-NN
A-NN
V-SFM
A-SFM
97.00
95.38
91.60
93.18
94.99
96.01
82.96
83.94
80.87
82.77
82.49
84.92
76.93
76.09
63.14
74.94
72.75
75.83
65.71
57.97
38.38
54.09
58.20
59.57

49. Average Number of Selected Features
LP SVM
NLP SVM
V-SFM
A-SFM
WDBC 30
11.6
8.5 HD 13
7.4
8.7
PID 8
4.3
4.5
BLD 6
3.3
3.7

50. Medical Data Signal Processing Apparatus (MeDSPA)
Quantitative analyses of medical data
Neurophysiological data (e.g., EEG, fMRI) acquired during brain diagnosis
Envisioned to be an automated decision-support system configured to accept input medical signal data (associated with a spatial position or feature) and provide measurement data to help physicians obtain a more confident diagnosis outcome.
To improve the current medical diagnosis and prognosis by assisting the physicians
recognizing (data-mining) abnormality patterns in medical data
recommending the diagnosis outcome (e.g., normal or abnormal)
identifying a graphical indication (or feature) of abnormality (localization)
We envision the outcome of our research in medical diagnosis as a tool or apparatus to process medical data signal
This is just my vision but we still have a long way to go.
We have started off with neurophysiological signals like electroencephalograms or fMRI –
Then use the tools developed over the course of my research as an automated decision support systems for physicians to help
Recognize abnormal data or abnormal patterns in medical data
Try to localize the source of abnormality
Recommend the diagnosis outcome – rather improve the confidence in the diagnosis

51. Automated Abnormality Detection Paradigm
Data Acquisition
Multichannel
Brain Activity
Optimization: Feature Extraction/ Clustering
Interface
Technology
Statistical Analysis:
Pattern Recognition
Nurse
Stimulator
Initiate a warning or a variety of therapies (e.g., electrical stimulation, drug injection)
User/Patient
Drug

52. Acknowledgement: Collaborators
E. Micheli-Tzanakou, PhD
L.D. Iasemidis, PhD
R.C. Sachdeo, MD
R.M. Lehman, MD
B.Y. Wu, MD, PhD
Students
Y.J. Fan, MS
Other undergrad students

53. Thank you for your attention!
Questions?