1. M ultichannel A nalysis of the N ewborn EEG D ata Vaclav Gerla*, Lenka Lhotska*, Member, IEEE, Vladimir Krajca**, Karel Paul*** * Czech Technical University - Department of Cybernetics, Prague - Czech Republic ** University Hospital Na Bulovce, Prague - Czech Republic *** Care of Mother and Child, Prague - Czech Republic http://gerstner.felk.cvut.cz gerlav@fel.cvut.cz

2. O ur R esearch P urpose Biological Signals Feature Extraction / Selection Classifier 1 Classifier N … Classifier 2 Visualisation Optimalization Classifiers Combining EEG, ECG, EOG, EMG, PNG Mainly FFT/Wavelets Various type of classifiers: Linear Models, Neural Networks, Kernel Methods, Mixture Models, … Weighted Average, Bagging, Boosting, Shafer approach, Fuzzy Integral, BKS * We solve problem of feature extraction and we compare various classifiers in this study Visualisation in all stages of this process

3. Motivation, approach usability online monitoring estimation of the newborn brain maturity In this study we use data: from 12 infants // 3 hours for each provided by the Institute for Care of Mother and Child in Prague Data are evaluated and scored by expert into 4 stages: quiet sleep active sleep wake movement artefact M otivation, U sed D ata proportion of these states is a significant indicator in clinical practice!

4. S ystem S tructure learning by EM PSD (band 0.5-3Hz) EEG, 8 channels PNG (respiration) measure of regularity ECG beat frequency EOG PSD (1-2Hz) EMG standart deviation 8 features HMM nearest neighbourcluster analysisdecision rules F1 F2 F3 features centering + Principal Component Analysis (12 features 3 features)

5. S egmentation EEG

6. EEG F eature E xtraction - classification obtained by doctor - record length = 85 minutes - features based on PSD - compute for each EEG channel - delta band is shown here (0.5 to 3Hz) - for subsequent processing we use these 8 characteristics - simple classification procedure example - used EEG signal only - based on proportion between activities in the different EEG channels (e.g.T3+T4/C3+C4)

7. EEG F eature E xtraction - PSD for other newborns signal - blue color = minimum & red color = maximum - maximum is in central electrodes (C3, C4)

8. R egularity of R espiration C urve - We utilize the strong regularity in quite sleep => autocorrelation analysis - clear difference in the magnitude of the second peak in the autocorrelation function - we use average breath duration for second peak position estimation

9. R egularity of R espiration C urve - characteristics for other newborns - it is no possible find one value for classification threshold - but it is good for doctors (as additional information )

10. E ye M ovements - we detect eye movements - derived from EOG signal Algorithm: 1. filter signal to freq. band 1-2Hz 2. compute STDs in small windows Utilized fact: In the quiet sleep there should not be any eye movements!

11. EMG A ctivity - obtained from chin EMG signal - computed STD of this signal - feature useful for movement artifact detection - we compute mean value for small window (removing peaks) and than we find maximum for bigger windows (trend enforcement) Utilized fact: Large majority of movement artifacts are present at EMG signal (characterized by the very high amplitude)

12. EMG A ctivity - muscles activity for other newborns - not present in quiet sleep

13. H eart R ate - derived from ECG - used standard method for QRS position detection based on first derivation - we detect maximum of R-peak The amplitude and the regularity of heart rate is changed during sleep!

14. H eart R ate - heart rate characteristics for other newborns - slow changes are visible - heart rate is lower in quiet sleep

15. P rincipal C omponent A nalysis reduce the number of dimensions without significant loss of information original features are very correlated -> PCA saves classification time PCA

16. H idden M arkov M odels in our case, HMMs allow us to describe relations between all features and hidden states (all sleep stages) we use the EM algorithm for finding the maximum-likelihood estimate of the parameters of HMMs choise of initial model is crucial - we compute it from the training data set mutual relations between individual hidden states

17. R esults Accuracy of classification: 2. We used data from 11 newborns for learning and data from remaining one newborn for testing. This procedure we repeated for all newborns and computed mean value. 1. We used all data from 12 newborns and cross-validation (10 group)

18. C onclusion our final accuracy obtained was about 70% on unknown data set compared with physician (evalution accuracy of physician is about 80%) very illustrative is to show final decision together with all described characteristics (we can see significant trends during sleep) during automated classification we have problem with clear separation of stages wake and active sleep. Now we try to find hidden information enabling this separation our designed technique can be applicable to other similar problem in medicine as well

19. in our further research we plan to develop methods for quantification that can help in evaluation of newborns brain maturity we expected increasing of accuracy and robustness by the combining all described classifiers. We plan use methods as bagging and boosting F uture W ork we plan to use similar methods for classification of sleep in adults we have developed hardware solution for on-line measuring of EEG (now we concentrate on the pda based analysis methods)

20. T hank you for your A ttention