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Feature Importance in Bayesian Assessment of Newborn BrainMaturity from EEG

Livia Jakaite, Vitaly Schetinin and Carsten Maple

Department of Computer Science and Technology

University of Bedfordshire

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Outline

EEG assessment of brain maturity

Why Bayesian Model Averaging (BMA) for the assessment?

Problems in using BMA for assessing brain maturity

Solution: using posterior information about features

Computational Experiments

Conclusions

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EEG assessment of brain maturity

Newborn brain dismaturity alerts about neurophysiologic abnormality

Experts can assess newborn brain maturity by estimating a newborns age from an EEG recording

The accuracy of such estimate is usually two weeks

Brain maturity is assessed as normal if the newborn’s physical age is within the range of EEG-estimated ages; otherwise the maturity is assessed as abnormal

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… EEG assessment of brain maturity:EEG examples for different ages

28 weeks

36 weeks

40 weeks

20 s

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BMA for brain maturity assessment

Bayesian Model Averaging (BMA), in theory, provides the most accurate assessments and estimates of uncertainty

In practice, Markov Chain Monte Carlo (MCMC) is used to approximate the posterior distribution by taking random samples

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…BMA for brain maturity assessment:exploring the posterior probability

An idea behind BMA is to average over multiple models diverse in their parameters

To ensure unbiased estimates, the portions of models sampled from the posterior distribution should be proportional to their likelihoods

The assessments will be most accurate, and the variation in models outcomes will be interpreted as the uncertainty in assessment

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• Change variable move• Change threshold move

• Combine 2 terminal nodes (death move)• Split a terminal node (birth move)

…BMA for brain maturity assessment:exploring the posterior probability

The exploration is made with moves chosen with predefined probability during a burn-in phase

Each move changes the model parameters and is accepted or rejected accordingly to Bayes’ rule

During a post burn-in phase, models are collected to be averaged

X1, 1

X2, 2

X5, 5

X3, 3

X4, 4

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…BMA for brain maturity assessment:lack of prior information causing biased sampling

To collect models proportionally, a model parameter space must be explored in detail

When the model parameter space is large, possible problem is:

Not all areas of PDF are explored, and then the models are disproportionally sampled

Prior information about feature importance helps to reduce a model parameter space

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However, in our case, no prior information on feature importance is available

The EEG data is represented by spectral features and their statistical characteristics, in total by 72 attributes, some of them make weak contribution

To assess the feature importance, we can use Decision Trees (DTs) for BMA

…BMA for brain maturity assessment:lack of prior information causing biased sampling

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If an attribute was rarely used in DTs included in the ensemble, we assume that this attribute makes a wear contribution

When the number of weak attributes is large, the disproportion in models becomes significant

Our hypothesis is that discarding the models using weak EEG attributes will reduce the negative effect of disproportional sampling

Solution: using posterior information about features

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Experiments

A BMA ensemble was collected from DTs learned from EEG data represented by the 72 attributes

We calculated the posterior probability of using each attribute in the DTs

We refined the DT ensemble from those DTs which use weak attributes

For comparison, we rerun BMA on the EEG without the identified weak attributes

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Performance of the BMA on age groups of 40 – 45 weeks

10 000 DTs

6 classes

Performance

27.4 ± 8.2

Entropy:

478.3 ± 15.8

40 45 40 45 40 45

40 45 40 45 40 45

43 w 44 w 45 w

40 w 41 w 42 w

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Posterior feature importance

Spectral powers Statistical characteristics

Delta Alpha Delta Alpha

Post

eri

or

0.06

0

0.03

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Performance of BMA with discarded attributes

Perf

orm

ance

, %29.0±8.527.4±8.2

Entr

opy 478.3±15.8 463.6±26.3

0.0 0.001 0.002 0.003 0.004 0.005Threshold

0.0 0.001 0.002 0.003 0.004 0.005Threshold

25.8±1.7

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Performance of BMA with the refined ensemble

27.4±8.2 29.2±7.9

Perf

orm

ance

, %

Entr

opy

478.3±15.8469.0±11.9

0.0 0.001 0.002 0.003 0.004 0.005Threshold

0.0 0.001 0.002 0.003 0.004 0.005Threshold

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Performance of BMA with the refined ensemble

Performance, %

Count

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Conclusions

The larger the number of weak attributes, the greater the negative impact on BMA performance

Reduction of the data dimensionality by discarding of weak attributes enabled improving BMA performance (1.6%) due to reducing a model parameter space

The proposed technique provides comparable improvement in performance (1.8%) without the need of rerunning the BMA

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Acknowledgements

This research is funded by the Leverhulme Trust

www.leverhulme.ac.uk