Orosco Lorena, Garces Correa Agustina, Laciar Eric
Gabinete de Tecnología Médica, Universidad Nacional de San Juan, Argentina.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:951-4. doi: 10.1109/IEMBS.2010.5627564.
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important tool for the diagnosis of epilepsy. In this study, an epileptic seizure classification method based on features of the Empirical Mode Decomposition (EMD) of EEG records is proposed. The Intrinsic Mode Functions (IMFs) of EEG records are first computed, and then several time and frequency features of IMFs are extracted. A features selection based on a Mann-Whitney test and Lambda of Wilks criterion is performed, then these parameters are used in a linear discriminant analysis (LDA) to classify epileptic seizure and normal EEG segments. The algorithm was tested in 3 intracranial channels EEG records acquired in 21 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The signal was divided in 15 s segments. In 45517 segments analyzed (689 with epileptic seizures) the sensitivity and specificity obtained with this method were 69.4% and 69.2% respectively. It could be concluded that the developed method could be a promising tool for epileptic seizure detection in EEG records.
癫痫是一种影响全球约5000万人的神经系统疾病。癫痫发作检测是癫痫诊断的重要工具。在本研究中，提出了一种基于脑电图（EEG）记录经验模态分解（EMD）特征的癫痫发作分类方法。首先计算EEG记录的本征模态函数（IMF），然后提取IMF的若干时域和频域特征。基于曼-惠特尼检验和威尔克斯λ准则进行特征选择，然后将这些参数用于线性判别分析（LDA），以对癫痫发作和正常EEG片段进行分类。该算法在21例难治性癫痫患者获得的3个颅内通道EEG记录中进行了测试，并由弗莱堡大学医院癫痫中心进行了验证。信号被分成15秒的片段。在分析的45517个片段（689个有癫痫发作）中，该方法获得的灵敏度和特异度分别为69.4%和69.2%。可以得出结论，所开发的方法可能是EEG记录中癫痫发作检测的一种有前景的工具。
