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to transition to another state. Figure 1.9 completes flow of EEG proposed EEG-based classification system.
1.5 Result and Discussion
1.5.1 Result
The proposed methodology is applied by making use of PYTHONIDE on Intel(R) Core(TM) i5-2410M CPU @ 2.30 GHz and 16 GB RAM. The performance evaluation of the researcher’s proposed HCFS-Hierarchical clustering is done on particular medical field disease since it affects lifetime motion inability. The statement of facts relating to EEG data is collected from different unsorted sources in various ways.
Figure 1.8 Pseudocode for proposed EEG prediction system.
A set of experiments were performed to determine characteristics about the response of ictal EEG data to several mathematical techniques. Finally, an emulator of a system that could take in a multi-channel EEG stream and return seizure status indicators was created. Table 1.4 represents EEG signal seizures proposed HANNSVM system result. The first experiment was to create and test a framework that could be used to transition between a number of states based on the predictions of HANNSVM classification algorithms [35–37]. This initial testing was done using the EEG data provided by the Stanford University Kaggle dataset. All subsequent experiments use the data provided by the Frieburg Seizure Prediction Project. This was done because it was easier to assess the accuracy of the system using several cases of seizure from the same patient. The second phase experiment focused on determining a feasible duration of the preictal state that best expose EEG characteristics that lead up to the ictal state.
Table 1.4 EEG signal seizures proposed HANNSVM system result.
In this experiment, training and testing data are unrelated and from different seizure cases, but all recorded and find the optimal feature sets that maximize the accuracy of the prediction. These experiments show the relative strength of each of the classification algorithms and also approximate the behavior of the state transitions [38, 39]. Tonic–clonic seizure activity is generally easy to see visually in an EEG as the amplitudes of the signal start to peak and a different set of frequencies are prevalent [40–42]. This served as an easy indicator of the workings of the seizure predictions system. Since there is no gold standard number of unique states to distinguish between in ictal pattern recognition, an initial guess of 5 states were defined in Table 1.5. This guess was made by visually determining sections of EEG signal that were most unique [43]. To keep any confounding variables constant, the training and testing data used were taken from the same seizure case where 80% was used as training, and 20% was used as testing (random new permutations for every classification) [44–46]. The distinction made between the Seizure Onset Period and the Preictal Period was done to determine if there was a more gradual transition between the preictal and ictal seizure states [47–49].
The results of this experiment were able to show the efficacy of the state decision neurons for making state transitions and the decision fusion which was used to improve the classification [50–53]. Also, a module was created to segment the multichannel EEG signal, apply a window function, and pass it on to the system at the appropriate time intervals. This made it possible to emulate a more realistic scenario. As a sub-study, the stepwise feature optimization algorithm is used in this experiment to determine the feature sets that result in the highest accuracy predicting the preictal state. Within the 100 s prior to seizure onset, time frame was given to localize the preictal state to a region of time that was not unreasonably short or long, but just enough for seizure intervention methods to be successfully executed [54–56].
Table 1.5 Initially defined epileptic states.
| State | Epileptic state |
| 1 | Normal, calm |
| 2 | Seizure onset period |
| 3 | Preictal |
| 4 | Medium seizure state |
| 5 | Full seizure state |
Figure 1.9 Graph between TPR vs TFR.
For this and all subsequent experiments, the data provided by the Seizure Prediction Project in Standford University is used for testing. In this experiment, the training and testing data were partitioned from the same dataset; 80% was used as training data and 20% was used as testing data. The states were redefined as seen in Table 1.5 for this, and all subsequent experiments. The epileptic state definitions for all the EEG streams (specifically, States 1, 2, and 4) were defined by the respective researchers who provided the data for this research. State 3, or the preictal state, was initially defined to be one second before State 4. In this experiment, the duration of the preictal state was iteratively increased to 100 s, and a series of classifications were done at each step for each brain wave [57, 58]. The best features for each iteration were saved. This gave an estimate as to how long a feasible preictal state (one that could be predicted) would be for each of the patients, and for each type of epilepsy [59]. Figure 1.9 displays the EEG seizure features prediction for True positive rate vs false positive rate (receiver operating characteristic curve).
1.5.2 Discussion
Using the knowledge gained from the previous experiments, a balance of all the algorithms features and other parameters were combined in order to come up with a prediction model capable of detecting the EEG state in Table 1.5 is discussed. In its fundamental form, this last experiment