M ultichannel A nalysis of the N ewborn EEG D ata

1. Multichannel Analysis of the Newborn EEG Data 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.czgerlav@fel.cvut.cz

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

3. Motivation, Used Data • 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 evaluatedand scored by expert into 4 stages: • quiet sleep • active sleep • wake • movement artefact proportion of these states is a significant indicator in clinical practice!

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

5. Segmentation EEG

6. EEGFeature Extraction - 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. EEGFeature Extraction - PSD for other newborns signal- blue color = minimum& red color = maximum- maximum is in central electrodes (C3, C4)

8. Regularity of Respiration Curve - We utilize thestrong 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. Regularity of Respiration Curve - characteristics for other newborns- it is no possible find one value for classification threshold - but it is good for doctors (as additional information )

10. Eye Movements - 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 Activity - 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 Activity - muscles activity for other newborns- not present in quiet sleep

13. Heart Rate - derived from ECG- used standard method for QRS positiondetection based on first derivation- we detect maximum of R-peakThe amplitude and the regularity of heartrate is changed during sleep!

14. Heart Rate - heart rate characteristics for other newborns- slow changes are visible- heart rate is lower in quiet sleep

15. Principal Component Analysis • reduce the number of dimensions without significant loss of information • original features are very correlated-> PCA saves classification time PCA

16. Hidden Markov Models • 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. Results Accuracy of classification: 1. We used all data from 12 newborns and cross-validation (10 group) 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.

18. Conclusion • 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. Future Work • 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 • 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. Thankyou for your Attention