Paper
12 April 2005 The signal extraction of fetal heart rate based on wavelet transform and BP neural network
Xiao Hong Yang, Bang-Cheng Zhang, Hu Dai Fu
Author Affiliations +
Proceedings Volume 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics; (2005) https://doi.org/10.1117/12.621935
Event: Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, 2004, -, Singapore
Abstract
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiao Hong Yang, Bang-Cheng Zhang, and Hu Dai Fu "The signal extraction of fetal heart rate based on wavelet transform and BP neural network", Proc. SPIE 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, (12 April 2005); https://doi.org/10.1117/12.621935
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KEYWORDS
Fetus

Wavelet transforms

Neural networks

Signal processing

Heart

Interference (communication)

Wavelets

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