摘要
宫缩状态实时识别在分娩镇痛中具有重要意义,但相关传统算法和系统无法满足实时识别宫缩状态的要求。针对上述情况,本文设计了一套宫缩状态实时分析算法。该算法包括宫缩信号预处理、基于直方图和线性迭代的宫缩基线估计以及一种基于有限状态机原理的实时识别算法,可根据前一点的宫缩状态以及一系列状态转换条件来识别当前的宫缩状态,并且设置缓冲机制来避免不真实的状态转换。为了评估该算法的性能表现,本文将其与现有的一种电子胎儿监护仪的宫缩分析算法进行比较。实验结果表明,本文算法能够在宫缩信号实时监测的同时对宫缩状态进行实时分析,算法敏感度为0.939 9,阳性预测值为0.869 3,具有较高的准确度,可达到临床监测的要求。
Identification of real-time uterine contraction status is very significant to labor analgesia, but the traditional uterine contraction analysis algorithms and systems cannot meet the requirement. According to the situations mentioned above, this paper designs a set of algorithms for the real-time analysis of uterine contraction status. The algorithms include uterine contraction signal preprocessing, uterine contraction baseline extraction based on histogram and linear iteration and an algorithm for the real-time analysis of uterine contraction status based on finite state machines theory. It uses the last uterine status and a series of state transfer conditions to identify the current uterine contraction status, as well as a buffer mechanism to avoid false status transitions. To evaluate the performance of the algorithm, we compare it with an existing uterine contraction analysis algorithm used in the electronic fetal monitor. The experiments show that our algorithm can analyze the uterine contraction status while monitoring the uterine contraction signal in a real-time. Its sensitivity reaches 0.939 9 and its positive predictive value is 0.869 3, suggesting that the algorithm has high accuracy and meets the need of clinical monitoring.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2017年第5期738-744,751,共8页
Journal of Biomedical Engineering
基金
国家国际科技合作专项资助项目(2015DFI12970)
粤港共性技术招标资助项目(2013B010136002)
广东省科技计划应用型科技研发专项资助项目(2015B020233010)
广东省科技计划重点资助项目(2015B020214004)
关键词
宫缩状态
基线估计
有限状态机
实时识别
uterine contraction status
baseline estimation
finite state machines
real-time identification
