Vladimir Krajca 1 , Jiri Hozman 1 , Jitka Mohylová 2 , Svojmil Petránek 3

1. Pattern recognition of epileptic EEG graphoelements with adaptive segmentation, supervised and unsupervised learning algorithms Vladimir Krajca1, Jiri Hozman1, Jitka Mohylová2, Svojmil Petránek3 1Czech Technical University in Prague, Faculty of Biomedical Engineering, Czech Republic, vladimir.krajca@fbmi.cvut.cz 2 VŠB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Czech Republic, jitka.mohylova@vsb.cz 3 Hospital Na Bulovce, Dept. Neurology, Prague, ebupetranek@seznam.cz

2. Introduction • The electroencephalogram (EEG) provides markers of brain disturbances in the field of epilepsy. • In short duration EEG data recordings, the epileptic graphoelements may not manifest itself. • The visual analysis of lengthy signals is a tedious task.It is necessary to track the EEG activity on the computer screen and to detect the epileptiform graphoelements. • The automation of the process is needed. • The EEG wave classification both by supervised and unsupervised learning algorithms will be compared. • Combinationoftheabovealgorithms will be used

3. Aimof study • To show, that artificial neural networks (ANN) exhibit better precision of classification of EEG graphoelements, then cluster analysis used perviously • Cluster analysis can be used in preprocessing – in semi-automatic creation of etalons for learning classifiers • Etalons can be extracted both manually and automatically from original EEG recordings – from segments detected by adaptive segmentation and described by a feature set from the time, frequency, and entropic domains.

4. Automaticidentificationof EEG graphoelements • In different areas of EEG processing, as • Brain maturation assesemnt of the newborns • Monitoring and detection of epileptic seizures in adults computerized analysis of micro- and macrostructure of EEG is desirable. • EEG microstructure – identification of single graphoelements and /or frequency bands, EEG bursts, artefacts, etc. • Macrostructure – trends, detection of significant events, behavioral states, sleep stages, reveals hidden information in long-term EEG processing

5. Cluster analysis and adaptive segmentation yield color identification of the classes. It reflects microstructure (short events). Temporal profiles reflect macrostructure, classs membership in the course of a time

6. Macrostructure is reflected in temporal profiles (example: time scale 15 min/page) SIGNIFICANT EVENT (artefact) SIGNIFICANT EVENT (epi paroxysms)

7. Cursor in profile (15 min/page) selects event in original EEG recording (at that position). Example: muscle artefacts (blue color) ORIGINAL EEG 10s/page CURSOR PROFILE (15min/page)

8. Example – epieventatcursorposition

9. Example – epilepticevents are reflected in temporal profile

10. Adaptivesegmentation and identifiedclustersimprovefeatureextraction and etalonsselection (wecan use as a guide segment boundaries and types/classesofsegments)

11. Cluster analysis • Advantages: unsupervised learning („push the button and wait for results“), classes are ordered according the increasing amplitude of segment • Disadvantages: classes (clusters) selected by a computer • Last (red class) can consist of genuine epileptic spikes, or there can be artefacts

12. Learning (supervised) classifers • Advantages: by supervised learning we can ourselves decide, which class is the first, second, etc. We can decide (by teaching) which types of graphoelements we are looking for. One class can consist of moving artefacts, which can be later eliminated • Disadvantages: teaching of classifier and etalons (prototypes) selection is a tedious work, requiring a skilled expert.

13. Expert in semi-automaticetalonsselection • Best compromise between visual and full-automatized EEG analysis is semi -automatic method, using both machine learning and expertise of the physician • As a first, preprocessing step, cluster analysis is used for etalons extraction: it is effective, but the classes are created independently on a user wishes. They can be inhomogeneous.

14. Learningclassifier • Teachingistedious. • Etalons – typicalrepresentativesofthedesiredclassesmustbecreated/selected by a teacher. • Etalons are submitted to classifierduring a learningprocess. At least 50-100 prototypes/class are necessary (personalexperience) • Manualprototypesselectionistime-consuming: but wecanexploitclasscentersoftheclustersforautomatic prototype selection – outliers are edited by an expert.

15. Automaticclassificationof EEG graphoelements by a cluster analysis • Efficient, without necessity of learning • Hybrid segments with overlapping classes exhibiting features of several classes can be misclassified. • No posiibility to influence classification – to specify uswer defined classes (artefacts in last class etc.) Clusters are created by „natural“ data structure • Clusters have spheric shape in the feature space, are formed without the user intervention.

16. Testingthemethodology on thereal data • EEG record of patient with the diagnosis epilepsy (length 31 min , 8 classes) • Both epileptic graphoelements and impulse artefacts have similar parameters (features).

17. Cluster analysis Noise/muscle artefacts are misclassified into blue (6th) class of impulse artefacts. See its position in temporal profiles. Note the good identification of continuous impulse artefacts in 13th channel.

18. Cluster analysis Misclassified „hybrid“ segments exhibiting features of both classes. Blue and violet are the class colors Fuzzy cluster analysis might help to improve to eliminate the hybrid segments

19. Cluster analysiscanbeusedforsemi-automaticextractionofetalonsfromtheraw, original EEG • Typical, representative segments of the cluster are positioned in feature space near the center of gravity. • They are typical members of the class (etalons) of the class, closest to the class center . • Because cluster analysis works relatively quicky, we have at our disposal the candidates for etalons . Only minimum effort is needed for final editing of the etalons set.

20. Cluster analysis Representativesegments , closest to the center of cluster = etalonsforteachingofthelearningclassifier (neural network)

21. Learningclassifierscouldprovidethesolution/improvement to theabovementionedproblems. Method: • User specifies what to search for • Realisation is performed by ANN (artificial neural networks) • Learning by GA (genetic algorithms) • Weights initializing (to avoid local minimum) - simulated annealing

22. ANN 24-12-8 • 24 inputs - features • 12 neurons in hidden layer (input features combining , set empirically – try and mistake approach • 8 outputs (8 classes)

23. ANN, 3-layer perceptron Improvementof cluster analysismethod – impulse and noisyartefacts are distinguishednow.

24. ANN, 3-layer perceptron Classes are more homogeneous now

25. How to selectetalons? • Expert selects etalons with a mouse on the computer screen • (semi) automatically by cluster analysis (minor editing of the etalons database)

26. ANN, etalonsselection Example of etalons selection – by mouse within the range (boundaries) of adaptive segmentation segments ETALON SPECTRUM FEATURES

27. ETALON SELECTION FROM EEG ANN, etalonsselection 2 1 - etalon selection 2 – etalon identification (classnumberentered by a teacher) 3 – click on the etalon – features histogram (4) andspectrum (5) Parameters are compared in smallwindow (6). Averagefeaturesandaveragespectrumforeachclass (7) 1 DATABASE EDITING 4 3 6 5 7

28. ANN, summarysheets Epileptic prototypes and artefacts in two different channels

29. Resultsvisualization Visualization – differenttypesofactivitycanbeidentified by a colordirectly in thereal EEG/temporalprofilesunderthecursorposition

30. ANN, etalonsselection

31. a b c d ANN - MLP(3- layerperceptron) Scatterogram AP- Sigma (amplitude vs. sigma frequency band) Features evaluation

32. Conclusion ANN with genetic algorithm and simulated annealing can learn to recognize the EG graphoelements much better than unsupervised learning algorithm. The types of graphoelements of classes can be specified by an user. Cluster analysis provides "natural" clusters, it is not possible to specify, that class number six, for example, consists of artifacts Cluster analysis can be used in the first step of processing - for etalons specification Adaptive segmentation can be used for manual selection of etalons from EEG for segment boundaries plotting in the graph

33. SHLUKOVÁ ANALÝZA Fuzzy shluková analýza (algoritmus FCM). Impulsy chybně zařazeny do třídy epi grafoelementů. Práh 0.3 ZLEPŠENÍ HOMOGENITY DAT. NETYPICKÉ SEGMENTY JSOU VYLOUČENY

34. Fuzzy shluková analýza - eliminace outliers s menším členstvím než 0.3

35. ANN - MLP(3-vrtstvý perceptron) Pro hodnocení kvality navržených příznaků lze užít histogram příznaků a spektrum. Z obr. 11 je opět patrné, že třídy č. 5 a 7 (počítáno od nuly) by se měly sloučit.

36. ARTEFAKTY Ale !!! : Artefakty - mohou spadnout do poslední třídy, stejně jako pomalá vysokovoltážní aktivita a poškodit přesnost detekce

37. Automaticclassification and visualizationofepileptic EEG by supervised and unsupervisedalgorithms

38. MOTIVACE – vedlejší paroxysmus

39. Srovnání fuzzy k-NN a Shlukové analýzy

42. Cluster

44. Fuzzy k-NN

45. Problems to besolved • Etalons description – features selection • Database of prototypes • Generalization – presented examples based on etalons extracted from the beginning of the same recording • Robust identification • Optimal MLP structure (number of hidden neurons) • Modern better classifiers inspired by a nature (genetic algorithms, ant colony optimization,…).