Electroencephalogram (EEG) signals are widely used in diagnosis of epilepsy. Accurate classification of seizure types based on EEG signals can provide vital information for diagnosis and treatment. Since visual inspection and interpretation of seizure types are time consuming and prone to errors, a novel classification method combining wavelet packet decomposition (WPD) and local detrended fluctuation analysis (L-DFA) is proposed for the computer-aided diagnostic system. The proposed method is able to classify a wide variety of seizures automatically and accurately. As the first step towards this goal, raw EEG signals are decomposed by WPD according to intrinsic frequency bands of human brain. Then L-DFA is applied to characterise the dynamical fractal structure of sub-band signals. Finally, EEG signals are classified by support vector machine based on the combined fractal spectrum features. The experimental results on Temple University Hospital database show that the proposed method achieves a total classification accuracy of 97.80%, outperforming existing methods based on the same database.

中文翻译：

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使用小波包分解和局部去趋势波动分析准确分类癫痫发作类型

脑电图（EEG）信号广泛用于癫痫的诊断。根据脑电信号准确分类癫痫发作类型可以为诊断和治疗提供重要信息。由于癫痫类型的视觉检查和解释既费时又容易出错，因此针对计算机辅助诊断系统提出了一种结合小波包分解 (WPD) 和局部去趋势波动分析 (L-DFA) 的新分类方法。所提出的方法能够自动准确地对各种癫痫发作进行分类。作为实现这一目标的第一步，WPD 根据人脑的固有频段对原始 EEG 信号进行分解。然后应用L-DFA来表征子带信号的动态分形结构。最后，支持向量机基于组合的分形谱特征对脑电信号进行分类。在天普大学医院数据库上的实验结果表明，该方法的总分类准确率达到了 97.80%，优于基于相同数据库的现有方法。