Procedimientos de cobranza

In the first few chapters of the thesis, we saw that successful prediction of epileptic seizures from EEG records can lead to better management of seizures. We also saw how machine learning tools can be used in order to improve such systems. The experimental part of this thesis was divided into three main categories: seizure detection under reduced dimensionality; the prediction of seizures in advance of the onset and multi-patient seizure prediction. The following, highlights the main contributions made across these experiments:

• Evaluating the effects of feature-set dimensionality across all patients: We used two filter feature selection methods in a stepwise dimensionality reduction experiment on the feature-set derived from Costa et al. (Costa et al. 2008) of all patients in the Freiburg EEG Database. We were able to evaluate the effects of reducing and increasing the size of the feature-set across all patients. We discovered that a feature-set of size 4 was the lowest possible size for epileptic seizure prediction studies, without significantly compromising the performance outcome of the classifier. This is while a large number of studies in the literature have reported their results on a single feature. Our results justify the recommendation that research in this field should move towards routinely considering multiple EEG features since these will improve outcomes (see sections 6.2).

• Identification of best EEG features:

We used filter feature selection tools to identify the ranking of the features according to the relevant feature selection criteria. We discovered that using good feature selection algorithms and stepwise feature-reduction, we were able to find groups of features that contributed the most to the individualised seizure classification across all patients (see section 6.2-6.3).  

• Extending the original Feature-set:

Drawing from the seizure prediction and dimensionality reduction experiments on single-channel and multi-channel feature-sets (sections 6.2, 6.3), we heuristically expanded the feature-set to include an additional 20 features per channel. The results revealed that performance of several subsets of the

extended feature-set, is higher than all other experimental results, including the benchmark (sections 6.4). The outcome was reported on an unseen test-set, where Accuracy, Sensitivity and Specificity were high.

• Identifying the high performance experimental setup for dimensionality reduction

We implemented our stepwise dimensionality reduction on a number of settings including: single-channel, multi-channel, extended feature-set. We discovered that out of all experiments, a subset of the extended multi-channel feature-set, yielded the highest performance on the held-out test-set. The results were validated in terms of S1-Score and Accuracy (sections 6.2-6.4).

• Prediction of seizures in advance of the onset

We used an advance prediction algorithm in conjunction with a suitable machine learning algorithm, in an effort to identify seizures in advance of their occurrence. The results revealed that advance prediction could be achieved with a relatively high performance, up to 25 minutes in advance of the seizure onset. The highest predictive performance was produced on a 14 dimensional subset of our extended feature-set (presented in chapter 6), determined by ReliefF feature selection method, using the Delete prediction algorithm. High values of S1- Score were obtained at t = 1 (96.30%) and t = 8 (96.13), the former being higher than the performance at seizure onset (96.18%), and where t is the minutes in advance of the seizure onset. We also identified intervals where advance prediction yielded a higher performance than the benchmark i.e. seizure onset detection. We revealed that for t minutes in advance of the seizure on-set, where t = 5 and 18, for all experiments, averaged over all Patient-Files, performance noticeably dropped. We also established that moments t = 1 and 2 constantly yielded high performance outcome across all experiments, and moments t = 8 and 16 were within a high-performance range for some of the best performing experiments. These findings suggest that these moments bear considerable predictive impact for a large population of patients (see section 7.2-7.5).

• Identifying the high performance experimental setup for advance seizure prediction

The advance seizure prediction was conducted on a number of experimental feature-sets: single-channel, multi-channel, extended multi-channel feature-set and a subset of 14 features from the extended feature-set. The subset of extended feature-set produced the highest outcome and yielded a relatively constant performance throughout all prediction time-frames. This was followed by the two cases of ‘best’ EEG channel advance prediction. In general, we recommend the construction of a large, heuristically constructed feature-set, from which, by using a good feature selection method such as ReliefF, a highly discriminatory set of features can be extracted according to the characteristics of the individual patients (see section 6.4, 7.4-7.5).  

• Validation of results:

In all experiments, classification results were averaged over 10 runs of training and testing. The classifiers were validated on a validation set and tested on a held-out test-set. The results were reported in terms of Accuracy and S1-Score (the harmonic mean of Sensitivity and Specificity). These characteristics of our experiments support the soundness of the presented contributions made by this thesis (Chapters 6, 7 & 8).

• Comprehensive evaluation of multi-patient seizure prediction:

The literature presents little evidence of extensive multi-patient seizure prediction analysis. The majority of seizure-prediction studies, has either dismissed the potential power of this tool, or has reported poor outcomes on a small set of patients. In this thesis, we conducted a full and exhaustive multi- patient seizure prediction analysis using Multi-class SVM, which has been used throughout this study. A comprehensive study entails the analysis of all possible training-set group-sizes with a reasonable number of combinations of patients in each group. These results were reported over 10 runs per classification. A total of 1102 classifiers were built for this experiment and results were reported in terms of ‘zero-training’ files, where patients on whom the classifier was not

trained were tested, and ‘Unseen trained’ files where 30% held-out data of the patients in the training-set were tested by the classifier.

The results were consistent with those in the literature: using a generally good machine learning algorithm, the classification of zero-training Patient-Files is significantly lower than the individualised seizure prediction algorithm. But in addition, we also revealed that group-size has a principal influence (albeit little) on both variations of test-set performance. Generally speaking, by adding more Patient-Files to the training-set, the generalisation of ‘zero-training’ files improves while the generalisation over ‘unseen-trained’ files decreases (see section 8.2).  

• Using suitable machine learning algorithms, multiple-patient seizure prediction can be improved

We implemented a number of advanced machine learning algorithms, which are more suitable for multi-source training and classification. From which, we were able to demonstrate that, by using a better machine learning algorithm (such as Deep belief Nets and Multi-Task Learning) and by handling skewness in the dataset, we are able to better generalise seizure detection for zero-training patients (see section 8.5).

• Ability to generalise and be generalised varies amongst patient

By closely observing results from the multi-patient analysis, where the sizes of training groups were 2 and 20, we revealed that the ability to generalise, and the tendency to be generalised as zero-training data, varies from patient to patient. More so, these two are not correlated for any single patient; a patient whose classifier does not generalise zero-training files well, may or may not be well- generalised by other classifiers. This was further verified in the enhanced multi- patient analysis using Multi-task learning and Deep Belief Nets, where the mean performance was less than the maximum performance produced by a single- patients’ generalisation (see section 8.4).