Signal Processing and Communications Department, University Carlos III of Madrid, Madrid, Spain.
Med Biol Eng Comput. 2010 Apr;48(4):321-30. doi: 10.1007/s11517-010-0590-5. Epub 2010 Mar 9.
This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time-frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.
本文提出了一种新的方法，通过时频分布（TFD）中的特征提取来识别脑电图（EEG）信号中的癫痫发作。特别是，该方法使用从 McAulay-Quatieri 正弦模型估计的轨迹从平滑伪魏格纳-维尔分布中提取特征。所提出的特征是主轨迹的长度、频率和能量。我们使用多个数据集评估了所提出的方案，并计算了敏感性、特异性、F 分数、接收机操作特性（ROC）曲线和百分位自举置信度，以得出结论，所提出的方案具有良好的泛化能力，是一种适用于以中等成本自动检测癫痫发作的方法，同时也为制定新的标准以检测、分类或分析异常脑电图开辟了可能性。
