The signal extraction of fetal heart rate based on wavelet transform and BP neural network

被引:0
|
作者
Hong, YX [1 ]
Cheng, ZB [1 ]
Dai, FH [1 ]
机构
[1] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130021, Peoples R China
关键词
fetal heart rate; wavelet transform; pattern recognition; BP Neural Network;
D O I
10.1117/12.621935
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed.8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.
引用
收藏
页码:914 / 920
页数:7
相关论文
共 50 条
  • [21] Purity identification of maize seed based on discrete wavelet transform and BP neural network
    Cao, Weishi
    Zhang, Chunqing
    Wang, Jinxing
    Liu, Shuangxi
    Xu, Xingzhen
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2012, 28 (SUPPL. 2): : 253 - 258
  • [22] Neural network based detection of fetal heart rate patterns
    Warrick, P
    Hamilton, E
    Macieszczak, M
    PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 2400 - 2405
  • [23] Classification of fetal heart rate tracings based on wavelet-transform & self-organizing-map neural networks
    Vasios, G
    Prentza, A
    Blana, D
    Salamalekis, E
    Thomopoulos, P
    Giannaris, D
    Koutsouris, D
    PROCEEDINGS OF THE 23RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: BUILDING NEW BRIDGES AT THE FRONTIERS OF ENGINEERING AND MEDICINE, 2001, 23 : 1633 - 1636
  • [24] Fetal heart rate extraction from composite maternal ECG using complex continuous wavelet transform
    Karvounis, EC
    Papaloukas, C
    Fotiadis, DI
    Michalis, LK
    COMPUTERS IN CARDIOLOGY 2004, VOL 31, 2004, 31 : 737 - 740
  • [25] Signal recognition based on wavelet and wavelet neural network
    Wu, YJ
    Shi, XZ
    Xu, M
    THEORETICAL ASPECTS OF NEURAL COMPUTATION: A MULTIDISCIPLINARY PERSPECTIVE, 1998, : 189 - 194
  • [26] Speech Signal Enhancement Using Neural Network and Wavelet Transform
    Daqrouq, Khaled
    Abu-Isbeih, Ibrahim N.
    Alfauori, Mikhled
    2009 6TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS AND DEVICES, VOLS 1 AND 2, 2009, : 826 - 831
  • [27] A new approach to recognize power quality disturbances based on wavelet transform and BP neural network
    Yao, Jiangang
    Guo, Zhifei
    Chen, Jinpan
    Dianwang Jishu/Power System Technology, 2012, 36 (05): : 139 - 144
  • [28] Inversion of Heavy Metal Content in Rice Canopy Based on Wavelet Transform and BP Neural Network
    Li X.
    Li L.
    Zhuang L.
    Liu W.
    Liu X.
    Li J.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2019, 50 (06): : 226 - 232
  • [29] Feature extraction using wavelet transform for neural network based image classification.
    Sarlashkar, MN
    Bodruzzaman, M
    Malkani, MJ
    THIRTIETH SOUTHEASTERN SYMPOSIUM ON SYSTEM THEORY (SSST), 1998, : 412 - 416
  • [30] Automatic Classification of Fetal Heart Rate Based on Convolutional Neural Network
    Li, Jianqiang
    Chen, Zhuang-Zhuang
    Huang, Luxiang
    Fang, Min
    Li, Bing
    Fu, Xianghua
    Wang, Huihui
    Zhao, Qingguo
    IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (02) : 1394 - 1401