The strength of this study in comparison to others is that it is a recent study and covers most of the recent research in seizure detection using data mining methods, Also, the time frame covers the most recent methods and techniques used for seizure detection. Some of the limitations encountered include, the number of studies that was gotten for the analysis, a good way to overcome this limitation will be to include other databases to see if more studies could be gotten. Also, some of the research articles were not accessible due to the need to subscribe to the journal site as they were on the subscribed list by the university and all efforts to get the full text proved abortive. Lastly, there was a huge number of diverse data mining classification algorithms or methods which requires extensive research and finally conducting a scoping review as a pair helps to give the study credibility and avoidance of selection bias, but as this is a masters thesis it was conducted by an individual.
53 7.3 Conclusion
In conclusion, data mining has found its use in a wide variety of areas which includes insurance, business and healthcare. In healthcare, it has found use in pattern recognition and in its ability to extract previously unknown information, which is why it became an interest area for research in epilepsy seizure detection. The manual process of analyzing EEG data has been a problem area in seizure detection because of the time and the differences in what constitutes a spike between neurophysiologists. This has prompted the use of data mining methods in the automatic detection of epilepsy to help solve this conundrum.
Overall, data mining could help neurophysiologists and physicians make vital decisions which in turn lead to better health outcome for people suffering from epilepsy This study identifies the use of EEG data in the literature as those aimed at seizure detection and at comparing the performances of the different data mining methods, also the different data mining methods used in seizure detection were highlighted and the data mining methods which can provide valuable information to physicians that can then be used in clinical decision support were identified. The findings of this study suggest data mining methods can be effective in seizure detection, several of the methods provided an accuracy or sensitivity level which shows these methods could be of immense benefit when applied in the clinical environment.
In the future there might be a need to develop a universal method which is fast, accurate, interpretable, cost-effective and accepted as the primary method in EEG seizure detection using data mining methods.
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67 APPENDICES
APPENDIX A: Data extraction table
Author,
Study aim Study topic Data source Applied Data
mining method
Method of feature extraction
Results Conclusion and comments affects the number of false positives.
Speed - Fast and can detect spikes in real time applied to EEG analysis for epilepsy seizure detection
Open-access data set from the University of Bonn, Epilepsy centre of the university hospital of Freiburg &
Hospital regional
Support vector machine & Bag of words
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across the 3 datasets.
Interpretability- not
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Public dataset from the University of Bonn Epilepsy centre (andrzejak et al 2001)
Classification The rule sets gotten by RF can be used as additional information for clinicians in the diagnosis of epilepsy
Classification The rule sets gotten by RF can be used as additional information for clinicians in the diagnosis of epilepsy