Arias-Ortega R, Gaitán-González M J, Yáñez-Suarez O
Universidad Autónoma Metropolitana - Iztapalapa, D.F., México.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2354-7. doi: 10.1109/IEMBS.2010.5627881.
An LMS-based algorithm to monitor fetal and maternal heart rate in real time was implemented and evaluated on a development platform. Hardware has three modules: dsPIC30F digital signal controller, a low-noise analog front end and a storage stage. They were evaluated using on-chip debugging tools and a patient simulator. Algorithm performance was tested using simulation tools and real data. Other measures like process run-times and power consumption, were analyzed to evaluate the design feasibility. Dataset was conformed by 25 annotated records from different gestational age pregnant women. Sensitivity and accuracy were used as performance measures. In general, sensitivity was high for maternal (95.3%) and fetal (87.1%) detections. Results showed that the chosen architecture can run efficiently the algorithm processes, obtaining high detection rates under appropriate SNR conditions.
在一个开发平台上实现并评估了一种基于学习管理系统（LMS）的实时监测胎儿和母亲心率的算法。硬件有三个模块：dsPIC30F数字信号控制器、低噪声模拟前端和一个存储阶段。使用片上调试工具和患者模拟器对它们进行了评估。使用模拟工具和实际数据测试了算法性能。分析了诸如处理运行时间和功耗等其他指标，以评估设计的可行性。数据集由来自不同孕周孕妇的25条带注释记录组成。敏感性和准确性被用作性能指标。总体而言，母亲检测的敏感性较高（95.3%），胎儿检测的敏感性也较高（87.1%）。结果表明，所选架构能够有效地运行算法流程，在适当的信噪比条件下获得较高的检测率。
