【摘要】：光電容積脈搏信號的峰值點自動識別直接關(guān)系到無創(chuàng)血氧飽和度測量與脈搏波峰-峰間期提取的準確率。提出一種小波聯(lián)合識別方法:基于小波多分辨率分析原理校正影響脈搏波峰值點幅值的基線干擾,再利用二次樣條小波模極大算法自動識別峰值點。將該方法應用到自行研制的光電容積脈搏波測量系統(tǒng)中,對采集的信號進行了校正與峰值點識別,通過在信號中增加隨機噪聲以評價方法的穩(wěn)定性與可靠性,然后利用10組實測數(shù)據(jù),對比本方法與傳統(tǒng)差分閾值法的峰值點識別準確率,進一步評價方法的有效性。結(jié)果表明:本方法在較好地消除了基線干擾的基礎(chǔ)上,在染噪的信號中仍然會較精確地檢測出脈搏波主波峰,具有較好的抗干擾能力,有利于提高血氧飽和度檢測及峰-峰間期提取的準確性,從而有助于后期人體呼吸功能評價與心率變異性分析。
[Abstract]:The automatic recognition of the peak point of the photoelectric volumetric pulse signal is directly related to the accuracy of the measurement of non-invasive oxygen saturation and the extraction of the peak and interpeak phase of the pulse wave. A wavelet joint recognition method is proposed: based on the principle of wavelet multi-resolution analysis, the baseline interference which affects the pulse peak amplitude is corrected, and the peak point is automatically identified by using the quadratic spline wavelet modulus maximum algorithm. The method is applied to the photoelectric volumetric pulse wave measurement system, and the collected signal is corrected and the peak point is identified. The stability and reliability of the method are evaluated by adding random noise to the signal. Then 10 groups of measured data are used to compare the accuracy of peak recognition between this method and the traditional difference threshold method to further evaluate the effectiveness of the method. The results show that on the basis of eliminating the baseline interference, the method can detect the main wave peak of pulse wave accurately in the noisy signal, and has good anti-jamming ability. It is helpful to improve the accuracy of oxygen saturation detection and peak-peak interval extraction, which is helpful to the evaluation of human respiratory function and the analysis of heart rate variability.
【作者單位】： 吉林大學儀器科學與電氣工程學院;吉林大學第一醫(yī)院;
【基金】：國家自然科學基金項目(41674108) 吉林省自然科學基金項目(20140101063JC) 吉林大學研究生創(chuàng)新基金項目(2016211)資助
【分類號】：R318;TN911.7
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本文編號：2227935