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Medical Diagnosis Decision-Support System: Optimizing Pattern Recognition of Medical Data W. Art Chaovalitwongse Industrial & Systems Engineering Rutgers.

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Presentation on theme: "Medical Diagnosis Decision-Support System: Optimizing Pattern Recognition of Medical Data W. Art Chaovalitwongse Industrial & Systems Engineering Rutgers."— Presentation transcript:

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 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

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

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

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 Mangasarian, Operations Research (1965); Bradley et al., INFORMS J. of Computing (1999)

6 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 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 Sensitive to noisy features Optimizing feature selection n features, 2 n 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

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 Normal Abnormal ? 9

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): 10

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

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 L i and L j ) in the k th element of the warp path. Dynamic Programming: The optimal warping distance is 12 Figure B) Is from Keogh and Pazzani, SDM (2001)

13 Optimizing Pattern Recognition 13

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 14

15 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)

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

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

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

19 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

20 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 Precision matrix, A contains elements of

21 Support Feature Machine 21

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

23 Application in Epilepsy Diagnosis 23

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

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 25

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 27

28 Normal versus Pre-Seizure 28

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

30 Normal versus Pre-Seizure Data Set EEG Dataset Characteristics Patient IDSeizure typesDuration of EEG(days)# of seizures 1CP, SC CP, GTC, SC CP ,SC CP, SC CP, SC CP, SC CP, SC CP, SC CP Total 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 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 Validation SFM N2 N3 N4 N5 P2 P3 P4 P Training Set Testing Set Selected Electrodes 34 P1N1 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)] 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

36

37 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)

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 39 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)

40 Epilepsy versus Non-Epilepsy 40

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 SFM E1 N2 N3 N4 N5 E2 E3 E4 E5 N Training Set Testing Set Selected Electrodes 42 N – Non-Epilepsy P – Epilepsy

43 Voting SFM: Validation 43

44 Averaging SFM: Validation 44

45 1 Fp1 – C3 16 T6 – Oz 17 Fz – Oz

46 Other Medical Diagnosis 46

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 47

48 Performance LP SVM NLP SVM V-SFM A-SFM WDBC HD PID BLD LP SVM NLP SVM V-NN A-NN V-SFM A-SFM Training Testing

49 Average Number of Selected Features LP SVM NLP SVM V-SFM A-SFM WDBC HD PID BLD

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) 50

51 Automated Abnormality Detection Paradigm User/Patient Interface Technology Multichannel Brain 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

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 52

53 Thank you for your attention! Questions? 53


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