全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

宫缩曲线分析及其状态实时识别算法的研究

DOI: doi:10.7507/1001-5515.201610021

Keywords: 宫缩状态, 基线估计, 有限状态机, 实时识别

Full-Text   Cite this paper   Add to My Lib

Abstract:

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

References

[1]  4. Ricci S S, Kyle T, Maternity and pediatric nursing. Lippincott Williams&wilkins, 2009: 370-373.
[2]  7. Bedwell C, Dowswell T, Neilson J P, et al. The use of transcutaneous electrical nerve stimulation (TENS) for pain relief in Labour: a review of the evidence. Midwifery, 2011, 27(5): e141-e148.
[3]  8. Horoba K, Matonia A, Jezewski J, et al. Analysis of uterine contraction activity using two ways of signal acquisition//XI Conference Medical Informatics&Technologies, 2006: 199-204.
[4]  9. Georgieva A, Payne S J, Redman C. Computerised electronic foetal heart rate monitoring in Labour: automated contraction identification. Med BiolEngComput, 2009, 47(12): 1315-1320.
[5]  11. van de Laar J. Kierkels J J M. Automatic analysis of uterine activity signals and application for enhancement of labor and delivery experience, WO2014/045221. A1. 2014. 03. 27.
[6]  17. Blumensath A, Gr?del E. Finite presentations of infinite structures: Automata and interpretations. Theory of Computing Systems, 2004, 37(6): 641.
[7]  18. Ayres-de-Campos D, Bernardes J, Garrido A, et al. SisPorto 2.0: a program for automated analysis of cardiotocograms. JMatern Fetal Med, 2000, 9(5): 311-318.
[8]  1. Vallejo M C, Firestone L L, Mandell G L, et al. Effect of epidural analgesia with ambulation on labor duration. Anesthesiology, 2001, 95(4): 857-861.
[9]  2. Zagami S E, Golmakani N, Saadatjoo S R, et al. The shape of uterine contractions and labor progress in the spontaneous active labor. Iran J Med Sci, 2015, 40(2): 98-103.
[10]  3. Klossner N J. Introductory maternity and pediatricnursing. Lippincott Williams & Wilkins, 2006. 176-177.
[11]  5. 邓松波, 陆尧胜, 方堃, 等. 生物反馈式经皮神经电刺激分娩镇痛系统的研制. 生物医学工程学杂志, 2015, 32(3): 667-6720.
[12]  6. Kaplan B, Rabinerson D, Lurie S, et al. Transcutaneous electrical nerve stimulation(TENS)for adjuvant pain-relief during labor and delivery. Int J GynaecolObstet, 1998, 60(3): 251-255.
[13]  10. 杨建平, 肖开选. 一种评价宫缩强度的小波能量值分析方法. 生物医学工程学杂志, 2012, 29(1): 80-830.
[14]  12. Huang Zifang, Shyu M L, Tien J M, et al. Prediction of uterine contractions using Knowledge-Assisted sequential pattern analysis. IEEE Trans Biomed Eng, 2013, 60(5): 1290-1297.
[15]  13. Lu Y, Bao L, Lu C, et al. A computer-aided analyzing system for fetal monitoring parameters//2010 3rd International Conference on Biomedical Engineering and Informatics. Yantai, China, 2010: 684-688.
[16]  14. Gorry P A. General least-squares smoothing and differentiation by the convolution(Savitzky-Golay)method. Anal Chem, 1990, 62(6): 570-573.
[17]  15. Dawes G S, Houghton C R S, Redman C W G. Baseline in human fetal heart‐rate records. Br J ObestetGynaecol, 1982, 89(4): 270-275.
[18]  16. Mantel R, van Geijn H P, Caron F M, et al. Computer analysis of antepartum fetal heart rate: 1. Baseline determination. Int J Biomed Comput, 1990, 25(4): 261-272.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133