Orosco Lorena, Laciar Eric, Correa Agustina Garces, Torres Abel, Graffigna Juan P
Gabinete de Tecnología Médica, Universidad Nacional de San Juan, San Juan, Argentina.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2651-4. doi: 10.1109/IEMBS.2009.5332861.
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method 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组成部分。在本研究中，提出了经验模态分解（EMD）方法用于开发自动癫痫发作检测算法。该算法首先计算脑电图记录的本征模函数（IMF），然后计算每个IMF的能量，并基于能量阈值和最短持续时间判定进行检测。该算法在弗赖堡大学医院癫痫中心提供并验证的9份侵入性脑电图记录上进行了测试。在分析的90个片段中（39个有癫痫发作），该方法获得的灵敏度和特异性分别为56.41%和75.86%。可以得出结论，EMD是一种用于脑电图记录中癫痫发作检测的有前景的方法。
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