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[1]郭垚垚,陈兆学.一种脉搏波和心电信号时域基线漂移消除方法[J].中国医学物理学杂志,2016,33(2):167.[doi:10.3969/j.issn.1005-202X.2016.02.012]
[J].Chinese Journal of Medical Physics,2016,33(6):167.[doi:10.3969/j.issn.1005-202X.2016.02.012]
[2]苏志刚,朱海玲,郝敬堂. 基于高斯混合模型的脉搏波特征提取方法[J].中国医学物理学杂志,2018,35(1):76.[doi:DOI:10.3969/j.issn.1005-202X.2018.01.014]
SU Zhigang,ZHU Hailing,HAO Jingtang. Gaussian mixture model-based method for extracting the features of pulse wave[J].Chinese Journal of Medical Physics,2018,35(6):76.[doi:DOI:10.3969/j.issn.1005-202X.2018.01.014]
[3]苏志刚,吕江波,郝敬堂. 基于平滑先验法去除脉搏波基线漂移[J].中国医学物理学杂志,2018,35(10):1197.[doi:DOI:10.3969/j.issn.1005-202X.2018.10.017]
SU Zhigang,LÜ,Jiangbo,et al. Removal of baseline drift of pulse wave based on smoothness prior[J].Chinese Journal of Medical Physics,2018,35(6):1197.[doi:DOI:10.3969/j.issn.1005-202X.2018.10.017]
[4]陈倩蓉,梁永波,赵飞骏,等. 基于心电-脉搏波的心血管疾病识别研究[J].中国医学物理学杂志,2019,36(2):210.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.017]
CHEN Qianrong,LIANG Yongbo,ZHAO Feijun,et al. Cardiovascular disease recognition based on electrocardiogram data and pulse wave[J].Chinese Journal of Medical Physics,2019,36(6):210.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.017]
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