Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo, China.
School of Information Technology, Deakin University, Melbourne, Australia.
Biomed Res Int. 2019 Jul 7;2019:5173589. doi: 10.1155/2019/5173589. eCollection 2019.
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), -nearest neighbour algorithm (-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 (). The proposed method outperforms the most recently reported methods in terms of in the same epileptic EEG database.
从脑电（EEG）信号中发现隐藏模式是有效检测癫痫发作的关键步骤。本研究提出了一种新的基于 Douglas-Peucker 算法（DP）和主成分分析（PCA）的方案，用于从癫痫 EEG 数据中提取有代表性和区分性的信息。由于多通道 EEG 信号高度相关且数据量大，因此 DP 算法被用于从 EEG 数据中提取最具代表性的样本。PCA 用于生成不相关的变量，并减少 DP 样本的维度，以提高识别能力。为了验证所提出方法的稳健性，我们使用了四种机器学习技术，随机森林分类器（RF）、-近邻算法（-NN）、支持向量机（SVM）和决策树分类器（DT），对获得的特征进行分类。此外，我们通过与一些最近报道的算法进行比较，评估了所提出方法的性能。实验结果表明，DP 技术可以有效地从 EEG 信号中提取代表性样本，压缩多达 EEG 信号的 47%以上的样本点。结果还表明，基于 RF 分类器的所提出特征方法的整体分类准确率达到了 99.85%（），优于相同癫痫 EEG 数据库中最近报道的方法。
