EEMD在同時消除脈搏血氧檢測中脈搏波信號高頻噪聲和基線漂移中的應(yīng)用

韓慶陽; 王曉東; 李丙玉; 周鵬驥

doi:10.11999/JEIT141390

EEMD在同時消除脈搏血氧檢測中脈搏波信號高頻噪聲和基線漂移中的應(yīng)用

doi: 10.11999/JEIT141390

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出版歷程
- 收稿日期: 2014-10-31
- 修回日期: 2015-02-06
- 刊出日期: 2015-06-19

Using EEMD to Eliminate High Frequency Noise and Baseline Drift in Pluse Blood-oximetry Measurement Simultaneously

摘要

摘要: 人體血氧飽和度是基于脈搏波信號測量得到的，然而在脈搏波信號采集的過程中存在著由人體呼吸和儀器本身熱噪聲等帶來的基線漂移和高頻噪聲，影響人體血氧飽和度的測量精度。因此，該文提出一種總體平均經(jīng)驗(yàn)?zāi)B(tài)分解(Ensemble Empirical Mode Decomposition, EEMD)與基于排列熵(Permutation Entropy, PE)的信號隨機(jī)性檢測相結(jié)合的方法，同時消除基線漂移和高頻噪聲。對脈搏波信號進(jìn)行EEMD分解，計(jì)算分解到得到的內(nèi)在模式分量的排列熵，選取閾值，分別判斷并剔除代表高頻噪聲和基線漂移的內(nèi)在模式分量。最后信號重構(gòu)就得到同時消除高頻噪聲和基線漂移的脈搏波信號。通過自行研制的測量裝置所采集的脈搏波信號進(jìn)行實(shí)驗(yàn)驗(yàn)證，利用信號的頻譜和交直流比R評價(jià)效果。結(jié)果表明：該方法有效地同時消除了脈搏波信號中的高頻噪聲和基線漂移，這將有利于人體血氧飽和度測量精度的提高。
- 脈搏波信號 /
- 人體血氧飽和度 /
- 高頻噪聲 /
- 基線漂移 /
- 總體平均經(jīng)驗(yàn)?zāi)B(tài)分解 /
- 排列熵
Abstract: The measurement of blood-oxygen saturation is based on the pulse wave signal, but there are many factors impact the accuracy of measurement, such as high frequency noise caused by instrument thermal noise and baseline drift caused by the breath. A method which combines Ensemble Empirical Mode Decomposition (EEMD) and Permutation Entropy (PE) is proposed, it can decrease high frequency noise and baseline drift. The pulse wave signal is decomposed by EEMD, the PE of each Intrinsic Mode Function (IMF) is calculated and the threshold value of PE is chosen. Then the IMFs which present high frequency noise and baseline drift are judged and decreased. Finally, the signal without high frequency noise and baseline drift is achieved. A self-developed measurement device is used to obtain the pulse wave for testing validation, and the signal spectrum and AC-DC modulation ratio value are adopted to evaluate the effect. The result shows that this method could effectively remove high frequency noise and baseline drift, which is conducive to improve the accuracy of blood-oxygen saturation.
- Pulse wave signal /
- Oxygen saturation /
- High frequency noise /
- Baseline drift /
- Ensemble Empirical Mode Decomposition (EEMD) /
- Permutation Entropy (PE)

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