Figure 1.
Invasive EEG recording of patient 2 from the Freiburg EEG Database.
The image corresponds to pre-seizure and seizure data. Each row displays one of the 6 channel recordings. The name of the relevant EEG channel is listed to the right of each signal. The EEG signals were visualised using EEGLAB software [30].
Figure 2.
Signal Energy over 6 EEG channels for patient 2 from the Freiburg EEG Database.
There is ictal activity from seconds 5 through 35. SE stands for Signal Energy.
Figure 3.
Accumulated Energy over 6 EEG channels for patient 2 from the Freiburg EEG Database.
There is ictal activity from seconds 5 through 35. AE stands for Accumulated Energy.
Figure 4.
An annotated epoch of the Invasive EEG of an epileptic seizure.
All four states of ictal, pre-ictal, ictal, post-ictal and inter-ictal are colour coded. EEG signals belong to patient 2 from the Freiburg EEG Database and were visualised using the EEGLAB software [30].
Figure 5.
The system consists of a Pre-processing Module and a Learning Module. The data preparation and initial experimental setup takes place in the Pre-processing Module, which varies for each experiment. This is separated from the learning and classification task in the Learning Module.
Figure 6.
Amendment of datasets for time-in-advance predictive models.
The top image displays a standard Ictal file where the ictal data is immediately preceded by a 300-seconds period of pre-ictal data, and also represents the dataset used to build ‘t = 0’ predictive models. The subsequent series of images then illustrates, from top to bottom, the datasets used to build the ‘t = 1’, ‘t = 2’, ‘t = 3’, ‘t = 4’ and ‘t = 5’ predictive models, constructed from the ‘t = 0’ model via removal of t minutes of immediately pre-ictal data, and relabeling (as pre-ictal) t minutes of inter-ictal data.
Figure 7.
Summary results of advance prediction by ASPPR on 21 patients.
The plot shows Accuracy, Sensitivity, Specificity and S1-Score averaged across all 21 patients at each prediction time-step. The plot also displays the minimum, mean, maximum and full feature-set values for the S1-Score measure as well as the benchmark S1-Score value.
Table 1.
Summary of ASPPR on 21 patients.
Figure 8.
Distribution of S1-scores over individual patients for each time-in-advance prediction model.
The boxes at each interval display the distribution of average S1-Score of each of the 21 patients at each advance prediction time-step.
Figure 9.
Mean S1-Score of the ASPPR algorithm for 21 patients.
The legend orders the patients in ascending order of their S1-Score averaged over all time-steps.