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-0546574, and Rutgers Computing Coordination Council (CCC).

Outline

Introduction 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

2

Pattern Recognition:Classification

3

Positive Class

Negative Class?

Supervised learning: A class (category) label for each pattern in the training set is provided.

Model-Based Classification Linear Discriminant Function

Support Vector Machines

Neural Networks

4

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8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes 10

Attributes

Samples

Class or Category

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

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

X X X

(a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor

K-nearest neighbors of a record x are data points that have the k smallest distance to x

Drawbacks

Feature Selection 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.

8

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

Normal

Abnormal?

9

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):

25

13

25 06.0)1(25

i

ii

i

10

Modified K-Nearest Neighbor for MDTS

11

Ch 1Ch 2Ch 3

Ch n

……

……

….

D(X,Y)

Time series distances: (1) Euclidean, (2) T-Statistical, (3) Dynamic Time Warping

AbnormalNormal

K = 3

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.

)(WDist

K

ktksk wwDistWDist

1,, ),()(

),( ,, tksk wwDist

Dynamic Programming:

30,30DThe optimal warping distance is

1,1,1,,,1min,, tsDtsDtsDLLDisttsD tj

si

12Figure B) Is from Keogh and Pazzani, SDM (2001)

Optimizing Pattern Recognition

13

Baseline Data

Signal Processing(Feature Extraction)

Extracted Features

Selected Featuresof All Baseline Data

Classifying New Samples

Feature Selection

Cleansed Data

Baseline Data

Signal Processing(Feature Extraction)

Selecting Good Baseline Dataand Deleting Outliers

Integrated Feature Selection & Pattern

Matching Optimization

Classifying New Samples

Optimally Selected Featuresof Optimized Baseline Data

Traditional Pattern-Based Classification Proposed Pattern-Based Classification

Cleansed Data

Extracted Features

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

14

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.

SFM: Averaging and Voting

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)

Distance Averaging: Training

Industrial & Systems Engineering Rutgers University

16

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.

m,...,,s 21

sj sj

ijdijd

1id

1id

2id imd

2idimd

Majority Voting: Training

Industrial & Systems Engineering Rutgers University

17

(Correct) if ; (Incorrect) otherwise. 1ija ijdijd

Negative Positive

iijd ijd

Feature j

0ija

Negative Positive

Feature j

jid jid i’

total number of samples.n total number of electrodes.m

SFM Optimization Model

1 if sample is correctly classified;

0 otherwise, for 1,..., .i

iy

i n

1 if electrode is selected;

0 otherwise, for 1,..., .j

jx

j m

average distance from sample to all other samples

in the same class, for 1... and 1... .

ijd i

i n j m

Intra-Class

average distance from sample to all other samples

in different class, for 1... and 1... .

ijd i

i n j m

Inter-Class

Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

1

11 1

21 1

1

max

s.t. for 1,...,

1 for 1,...,

1

0,1 for

n

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m m

ij j ij j ij j

m m

ij j ij j ij j

m

jj

j

y

d x d x M y i n

d x d x M y i n

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x j

1,...,

0,1 for 1,...,

i

m

y i n

Averaging SFM

Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

Maximize the number of correctly classified samples

Must select at least one electrode

Logical constraints on intra-class and inter-class distances if a sample is correctly classified

1

11 1

21 1

1

max

s.t. for 1,...,2

1 for 1,...,2

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or 1,...,

0,1 for 1,...,

0i

j m

y i n

Voting SFM

Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)

Maximize the number of correctly classified samples

Must select at least one electrode

Logical constraints: Must win the voting if a sample is correctly classified

1 if sample is correctly classified at electrode (good vote);

0 otherwise (bad vote), for 1,..., and 1,..., .ij

i ja

i n j m

Precision matrix, A contains elements of

Support Feature Machine

21

Step 1: For individual feature (electrode), apply the nearest neighbor rule to every training

sample to construct the distance and accuracy matrices

Step 2: Formulate and solve the SFM models and obtain the optimal feature (electrode)

selection

Step 3: Employ the nearest neighbor rule to classify unlabeled data to the closest baseline

(training) data based on the selected features

(electrodes)

UnlabeledSamples

Training Testing

Abnormal Samples

NormalSamples

x–electrode selectiony– training accuracy

–voting matrix–distance matrices

Support Feature Machine

Support Vector Machine

Feature 1

Feature 2

Feature 3

Ch 1Ch 2Ch 3

Ch n

……

……

….

1 2 3 4 nA data vector of EEG sample

……

Pre-Seizure

Normal

Application in Epilepsy Diagnosis

23

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.

*Begley et al., Epilepsia (2000); **Begley et al., Epilepsia (1994). 24

Simplified EEG System and Intracranial Electrode Montage

1 1

1 1

1 1

2 2

2 2

2 2

3 3

3 3

3 3

4 4

4 4

4 4

5 5

LTDRTD

LOF

LST

ROF

RST

1 1

1 1

1 1

2 2

2 2

2 2

3 3

3 3

3 3

4 4

4 4

4 4

5 5

LTDRTD

LOF

LST

ROF

RST

Electroencephalogram (EEG) is a traditional tool for evaluating the physiological state of the brain by measuring voltage potentials produced by brain cells while communicating

25

Scalp EEG Acquisition

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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

27

Normal versus Pre-Seizure

28

10-second EEGs: Seizure EvolutionNormal Pre-Seizure

Seizure Onset Post-Seizure

Chaovalitwongse et al., Annals of Operations Research (2006) 29

Normal versus Pre-SeizureData 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 7

3 CP 8.85 22

4 ,SC 5.93 19

5 CP, SC 13.13 17

6 CP, SC 11.95 17

7 CP, SC 3.11 9

8 CP, SC 6.09 23

9 CP, SC 11.53 20

10 CP 9.65 12

Total   84.71 153

CP: Complex Partial; SC subclinical; GTC: Generalized Tonic/Clonic

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 Seizure Duration of EEG

30 minutes 30 minutes

8 hours 8 hours 8 hours 8 hoursPre-seizure

Normal

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 10.24 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)

Time

EE

G V

olt

age

Evaluation 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)

Leave-One-Seizure-Out Cross Validation

SFM

N2

N3

N4

N5

P2

P3

P4

P5

1234567...

23242526

Training Set

Testing Set

Selected Electrodes

34

P1N1

N – EEGs from Normal StateP – EEGs from Pre-Seizure Stateassume there are 5 seizures in the recordings

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)]

Use each electrode as a base classifier 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)

35

Performance Characteristics:Upper Bound

37

SFM -> Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)NN -> Chaovalitwongse et al., Annals of Operations Research (2006)

KNN -> Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A (2007)

Separation of Normal and Pre-Seizure EEGs

From 3 electrodes selected by SFM From 3 electrodes not selected by SFM

Performance Characteristics:Validation

3939

SFM -> Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)SVM-> Chaovalitwongse et al., Annals of Operations Research (2006)

KNN -> Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A (2007)

Epilepsy versus Non-Epilepsy

40

Epilepsy versus Non-EpilepsyData Set

Routine EEG check: 25-30 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

5 sampled epochs

30 points 30 points

Epilepsy patients Non-Epilepsy patients

Elec 1

…..

150 points(25 minutes)

…..

Elec 2

Elec 17Elec 18

Leave-One-Patient-Out Cross Validation

SFME1

N2

N3

N4

N5

E2

E3

E4

E5

N1

1234567...

23242526

Training Set

Testing Set

Selected Electrodes

42

N – Non-EpilepsyP – Epilepsy

Voting SFM: Validation

43

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

k=5 k=7 k=9 k=11 k=All

Ove

rall A

ccura

cyVoting SFM Performance – Average of 10 Patients

DTW

EU

TS

KNN SFM KNN SFM KNN SFM KNN SFM KNN SFM

Averaging SFM: Validation

44

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

k=5 k=7 k=9 k=11 k=All

Ove

rall A

ccura

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Averaging SFM Performance – Average of 10 Patients

DTW

EU

TS

KNN SFM KNN SFM KNN SFM KNN SFM KNN SFM

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Selected Electrodes From Averaging SFM

Averaging SFM - DTW

Averaging SFM - EU

Averaging SFM - TS

Electrode

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1 Fp1 – C316 T6 – Oz17 Fz – Oz

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Other Medical Diagnosis

46

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

47

Performance

LP SVMNLP SVM

V-SFMA-SFM

WDBC 98.0896.17

97.2897.42

HD 85.0684.66

86.4886.92

PID 77.6677.51

75.0177.96

BLD 65.7157.97

63.4666.43

48

LP SVMNLP SVM

V-NNA-NN

V-SFMA-SFM

97.0095.38

91.6093.18

94.9996.01

82.9683.94

80.8782.77

82.4984.92

76.9376.09

63.1474.94

72.7575.83

65.7157.97

38.3854.09

58.2059.57

Training Testing

Average Number of Selected Features

LP SVMNLP SVM

V-SFMA-SFM

WDBC 3030

11.68.5

HD 1313

7.48.7

PID 88

4.34.5

BLD 66

3.33.7

49

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)

50

Automated Abnormality Detection Paradigm

User/Patient

InterfaceTechnology

MultichannelBrain Activity

Data Acquisition

Statistical Analysis:Pattern Recognition

Initiate a warning or a variety of therapies (e.g., electrical stimulation, drug injection)

Stimulator

Drug

Optimization: Feature Extraction/ Clustering

Nurse

Feature 1

Feature 2

Feature 3

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

52

Thank you for your attention!

Questions?

53