Guerrero-Mosquera Carlos, Vazquez Angel Navia
University Carlos III of Madrid, Signal Processing and Communications Department, 28911 Leganes, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:13-6. doi: 10.1109/IEMBS.2009.5332434.
This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.
本文描述了一种利用时频分布（TFD）进行特征提取的新方法，用于检测癫痫发作，以识别脑电图（EEG）中的异常情况。具体而言，该方法结合平滑伪维格纳-威利分布与麦考利-夸蒂里正弦模型来提取特征，并识别异常神经放电。我们基于轨迹长度提出了一种新特征，该特征与能量和频率特征相结合，能够在癫痫发作开始时，从其他振荡中分离出连续的能量轨迹。我们使用来自6名癫痫患者的16种不同发作的数据对我们的方法进行了评估。结果表明，我们的提取方法是一种适用于自动癫痫发作检测的方法，并为制定检测和分析异常脑电图的新标准开辟了可能性。
