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基于光照矫正的面部局部区域心率检测 被引量:5
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作者 桑海峰 金芷伊 《计算机应用》 CSCD 北大核心 2018年第A02期301-305,共5页
针对基于视频的非接触式心率信号易受光照干扰的问题,提出一种利用光电容积描记法(PPG)结合光照矫正和面部区域分割的非接触式心率信号检测方法。首先,对在正常光照环境下采集到的包含人体面部区域的视频进行人脸和背景分割,在面部区域... 针对基于视频的非接触式心率信号易受光照干扰的问题,提出一种利用光电容积描记法(PPG)结合光照矫正和面部区域分割的非接触式心率信号检测方法。首先,对在正常光照环境下采集到的包含人体面部区域的视频进行人脸和背景分割,在面部区域以眼睛为基准按比例选取感兴趣区域(ROI);其次,在RGB颜色模型中分别对背景区域和ROI区域提取绿色(G)通道像素均值获得原始信号并利用归一化最小均方自适应(NLMS)滤波算法进行光照矫正;最后,对去除环境光照干扰的心率信号进行时域滤波和频谱分析得到心率。经实验对比,基于NLMS去除光照干扰的视频心率检测在环境光强变化时检测稳定性好,误差率小于5%。 展开更多
关键词 光电容积描记法 归一化最小均方 自适应滤波算法 面部区域分割 心率信号提取
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The Signal Extraction of Fetal Heart Rate Based on Wavelet Transform and BP Neural Network
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作者 YANGXiao-hong ZHANGBang-cheng FUHu-dai 《Chinese Journal of Biomedical Engineering(English Edition)》 2005年第1期22-31,共10页
This paper briefly introduces the collection and recognition of bio-medical sig nals, designs the method to collect FM signals. A detailed discussion on the sys tem hardware, structure and functions is also given. Und... This paper briefly introduces the collection and recognition of bio-medical sig nals, designs the method to collect FM signals. A detailed discussion on the sys tem hardware, structure and functions is also given. Under LabWindows/CVI,the ha rdware and the driver do compatible, the hardware equipment work properly active ly. The paper adopts multi threading technology for real-time analysis and make s use of latency time of CPU effectively, expedites program reflect speed, impro ve s 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 sig nal in real-time. Wavelet transform to remove the main interference in the FM a nd by adding time-window to recognize with BP network; Finally the results of c ollecting signals and BP networks are discussed.8 pregnant women’s signals of F M were collected successfully by using the sensor. The correct of BP network rec ognition is about 83.3% by using the above measure. 展开更多
关键词 Fetal heart rate Wavelet transform Signal reco gnition BP neural network
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