Abstract：White cell signal (WBS) has various shapes of pulse and different timefrequency features, which makes it difficult to extact the physiological and pathological information from WBS using cell signal pulsecounting method and classify the blood cells accurately in clinics. In this work, the HilbertHuang transform (HHT) method which can adaptively decompose nonstationary and nonlinear signal was investigated to explore its application in WBS timefrequency analysis and classification. Using HHT, WBC′s intrinsic mode function (IMF), Hilbert marginal spectrum of IMF and Hilbert spectrum of WBS were obtained; Average intensity, spectral centric and the energy contribution rate of WBS of healthy people and patients were extracted and analysised through instantaneous frequency and instantaneous amplitude. According to the distribution of timefrequency features, the feature vector for classification experiments were constructed, and then support vector machine (SVM) was adopted in the classification of WBS experimental samples of 58 healthy persons and 60 patients. Results showed that the correct ratio of WBS classification was 94.83%. In conclusion, the HHT method is effective in extracting the WBS features and may be expected to assist clinical WBS processing and analysis.
张小飞 陶凌 邓娟 龙伟*. 基于希尔伯特-黄变换的白细胞信号分[J]. 中国生物医学工程学报, 2014, 33(1): 57-62.
ZHANG Xiao Fei TAO Ling DENG Juan LONG Wei*. The Analysis of White Blood Cell Signal Based on Hilbert-Huang Transform. journal1, 2014, 33(1): 57-62.
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