Thomas E M, Greene B R, Lightbody G, Marnane W P, Boylan G B
Dept. of Electrical Engineering, UCC, Cork, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:903-6. doi: 10.1109/IEMBS.2008.4649300.
The goal of neonatal seizure detection is the development of a patient independent system to alert staff in the neonatal intensive care unit of ongoing seizures. This study demonstrates the potential in adapting a patient independent classifier using patient specific data. Supervised adaptation is investigated using the basic gradient descent algorithm and least mean squares procedures. An increase in mean ROC area of 3% is obtained for the best performing learning algorithm, yielding an increase in mean accuracy of 7.7% compared to the patient independent algorithm.
新生儿惊厥检测的目标是开发一种独立于患者的系统，以提醒新生儿重症监护病房的工作人员注意正在发生的惊厥。本研究展示了使用患者特定数据来适配独立于患者的分类器的潜力。使用基本梯度下降算法和最小均方程序对监督适配进行了研究。对于性能最佳的学习算法，平均ROC面积增加了3%，与独立于患者的算法相比，平均准确率提高了7.7%。
