Faul Stephen, Temko Andriy, Marnane William
Department of Electrical Engineering, University College, Cork, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6612-5. doi: 10.1109/IEMBS.2009.5332553.
This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively show that high accuracy can be achieved independent of age. It is also shown that features contribute differently for neonatal and adult data.
本文研究了一种为新生儿数据开发的合适算法,是否也能原封不动地用于检测癫痫成人患者的癫痫发作。在17名新生儿患者和15名成人患者的数据库上评估了一种特征提取和支持向量机分类器系统的性能。新生儿和成人数据库的平均ROC曲线面积分别为0.96和0.94,这表明可以独立于年龄实现高精度。研究还表明,特征对新生儿和成人数据的贡献不同。