Further Discussion

Building on the promising work by Costa et al. (Costa et al. 2008) using 14 features extracted from the EEG, part of our study has involved investigating these features, to begin to assess their relative importance, as well as the potential for building classifiers that use fewer features (which carries the advantage, among others, of less bias towards over-fitting). We found that the use of mutual information was particularly well-suited to feature selection in this task, and it seems that promising results can be obtained, especially with the EANN, with only 8 of the 14 features. Analysis of feature rankings by mutinf showed particular prominence for features 1, 8, 9 and 14 (see Table 5.2). This suggests future research could concentrate on examining additional such features, respectively concerning the total accumulated energy in the signal, high-frequency aspects measured over short-term windows, low-frequency attributes measured over longer-term windows, and more metrics associated with Lyapunov exponents.

Other promising future work could explore wrapper techniques that attempt to derive an appropriate feature subset in tandem with the machine learning process.

The advance prediction results were similarly interesting. It seems clear that, among the 14 features used, detectable seizure-predictive patterns are present 8—10 minutes, as well as earlier, in advance of an ictal period. Both the MC- SVM and the EANN seem capable of constructing useful classifiers for 8—10 minutes advance prediction, with promising Sensitivity levels, at least in this single-patient context. However, more experimentation is required, with more patient data, in order to further validate these findings.

As discussed in the comprehensive recent survey by Mormann et al. (Mormann et al. 2006), the literature on seizure prediction studies is beset with a number of issues that confound progress, such as no standard approaches to experimental setup, large variation in the number and types of patients’ studies, and in the amount of data (EEG and otherwise) available per patient. Also, validation (i.e. obtain results for predictors on data unseen during training) is very often not done at all, since not enough data are available. Naturally, more sharing of data, and experimental design protocol, must be encouraged; whereas, reporting of results obtained on unseen data should typically be mandatory.

5.7 Summary

In this chapter we reviewed relevant results from the Costa et al. study where a 14 dimensional feature-set of a single patient from the Freiburg EEG Database was implemented in a number of experimental conditions with several Artificial Neural Networks. We evaluated the feature-set with two other classifiers, namely Multi-Class Support Vector Machine (MC-SVM) and Evolved Neural Network (EANN). The benchmark values on the ‘single’ patient scenario were similar to those produced by Costa et al.

We further evaluated the features with two feature selection methods: Clustub and Mutinf. We found that with a well-chosen reduced feature-set (using mutual information), promising results can be obtained with only 8 of the 14 features. Further analysis showed that the accumulated energy in the signal, the maximum Lyapunov exponent, as well as measures of high-frequency signal components measured over short term windows, seem most promising for future research into accurate advance prediction models.

In addition, we implemented an Advance Prediction algorithm where the prediction window was stretched over pre-determined timepoints. We observed that, using either a Multi-Class Support Vector Machine (MC-SVM) or an Evolved Neural Network (EANN), reasonable Specificity and Sensitivity could be achieved for prediction 8--10 minutes in advance of the seizure onset. Indications are that the EANN performance is preferable for advance prediction, however the results so far do not support this with statistical significance.

These results have served to indicate that we can achieve similar or better results to Costa et al. (Costa et al. 2008) using a similar (and hence impoverished) experimental regime. In future chapters we explore more comprehensive scenarios to validate these findings and make many new observations in the context of multiple patients and other scenarios and rigorously enhance and evaluate the Feature selection and Advance Prediction experiments.

Feature Selection and Dimensionality Reduction

In this chapter, we re-visit the importance of effective feature engineering in our seizure detection problem, drawing from our preliminary feature selection experiment presented in chapter 5. From the results in chapter 5, we concluded that using the correct feature selection method, we are able to produce a significantly smaller subset of features, for which the performance measures of the full feature-set are maintained. We also concluded that the contribution of certain features to the success of our seizure detection model is less than others.

In this chapter we present a number of experiments to further evaluate the established conclusions, under exhaustive experimental conditions. We hope to achieve a better understanding of the role of different features in the performance of our classifiers, and to determine the optimum feature settings under which, the performance of the model is at its highest value. We also aim to extend our feature-set based on heuristic results of experiments presented in this chapter, and further evaluate the performance of this extended feature-set. By determining the most effective features, we are able to build classifiers of increased efficacy and to clarify the role of EEG channels and features in successful seizure classification.

This chapter presents 3 experiments. All experiments were carried out on Patient-Files from the Freiburg EEG Database (as detailed in chapter 4), in a single- patient mode, where the classification model is built from, and tested on a single patient. Section 6.1 motivates us on why feature selection is important in machine learning problems, in particular, epileptic seizure detection studies. In experiment I, we apply the feature selection algorithms on each patient under default experimental settings presented earlier in chapter 5. The outcome of this step is a ranking of each feature based on algorithm-specific criteria; this is discussed in section 6.2.3. The ranking table will be further used in the same experiment to perform a stepwise dimensionality reduction on segments of each patient’s EEG which contain ictal (seizure) states recorded from a single focal channel (similar to that seen in chapter 5). In experiment II we perform the same feature selection and dimensionality reduction steps as experiment I, this time on an extended feature-set which is derived from all 6 recorded

EEG channels of each patient from the Freiburg EEG Database, referred to as Multi- Channel Patient-Files. In experiment III, we heuristically extend our feature-set, building on results of earlier experiments of this chapter. This is to comprise additional features, of similar characteristics to those features with the highest performance outcome. We further analyse the performance of our classifiers using the extended feature-set. In section 6.5, we discuss the outcome of the experiments and we present possible conclusions drawn from our results in section 6.6.

6.1 Motivation

As described in chapter 3, epileptic seizure detection and prediction from EEG recordings has been the focus of many studies in the field of computational neurology. The unpredictable nature of the seizure can impose potential risk for the individual with epilepsy. Therefore, the automatic detection of the seizure at the time of its occurrence or seconds before the neuronal onset, can give rise to timely intervention, minimising the risks involved.

The raw data recorded from EEG channels prior to pre-processing, is merely voltage outputs from each channel. This means that the data from the Freiburg EEG Database, in its raw format, has six features corresponding to the 6 recording channels. The small number of features and the large number of input data are disproportionate. Some studies have carried out automatic seizure detection algorithms on such data, mainly to showcase the power of the underlying machine learning algorithm, discarding the extensive feature engineering body of work, which could lead to potential algorithmic improvement (e.g. (Santaniello et al. 2011)). Amongst the studies that use feature engineering, the majority have i) used either non, or simple machine learning algorithms ii) have not conducted sufficient validation in terms of Sensitivity and Specificity iii) have evaluated the features individually (uni-variate analysis), instead of combining multiple features.

The problem in question encompasses the elements of both machine learning and feature selection problem by treating the question as a machine learning problem, we find the best learning model which yields the highest performance outcome on unseen data, and by posing the question as a feature selection problem, we are able to use the extensive body of work behind EEG feature engineering to further optimise the

learning algorithm. In other words, we seek to further improve seizure classification by optimising and improving the combination of features used in the learning algorithm. We aim to optimise the detection of seizure states through heuristic exploration of the default features from (Costa et al. 2008), and the introduction of some new features. The outcome of this chapter is the determination of best feature combinations per patient in addition to an optimum overall combination of features, considering the results from all patients. The latter will further be used for the extraction of additional features