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Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF),  k -nearest neighbour algorithm ( k -NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy ( OCA ). The proposed method outperforms the most recently reported methods in terms of  OCA  in the same epileptic EEG database.

Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals