光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
8期
2106-2111
,共6页
林凌%李威%周梅%曾锐利%李刚%张宝菊
林凌%李威%週梅%曾銳利%李剛%張寶菊
림릉%리위%주매%증예리%리강%장보국
经验模态分解%动态光谱%近红外%血红蛋白浓度%无创测量
經驗模態分解%動態光譜%近紅外%血紅蛋白濃度%無創測量
경험모태분해%동태광보%근홍외%혈홍단백농도%무창측량
Empirical mode decomposition (EMD)%Dynamic spectrum%Near infrared%Hemoglobin concentration%Non-invasive measurement
将经验模态分解(EMD)算法结合动态光谱理论中的频域提取算法用于血红蛋白浓度的无创测量。在体采集57例光电容积脉搏波,选取636.98~1086.86 nm范围内的光谱数据进行分析。首先通过EMD方法分别对各个样本每个波长的光电容积脉搏波进行去噪预处理,再利用离散傅里叶变换提取脉搏波的峰峰值构成动态光谱,最后运用偏最小二乘方法对各样本的动态光谱和血红蛋白浓度建立模型。与未经EMD处理的数据建模结果相比,EMD处理后,血红蛋白浓度预测集的相关系数从0.8798提高到0.9176,预测集均方根误差从6.6759 g·L-1减小到5.3001 g·L-1,相对误差从8.45%减小到6.71%,建模精度有了较大的提高。结果表明,采取经验模态分解的算法进行光电采集数据的去噪预处理可以提高光谱数据的信噪比,进而可以提高血液成分无创测量的准确性。
將經驗模態分解(EMD)算法結閤動態光譜理論中的頻域提取算法用于血紅蛋白濃度的無創測量。在體採集57例光電容積脈搏波,選取636.98~1086.86 nm範圍內的光譜數據進行分析。首先通過EMD方法分彆對各箇樣本每箇波長的光電容積脈搏波進行去譟預處理,再利用離散傅裏葉變換提取脈搏波的峰峰值構成動態光譜,最後運用偏最小二乘方法對各樣本的動態光譜和血紅蛋白濃度建立模型。與未經EMD處理的數據建模結果相比,EMD處理後,血紅蛋白濃度預測集的相關繫數從0.8798提高到0.9176,預測集均方根誤差從6.6759 g·L-1減小到5.3001 g·L-1,相對誤差從8.45%減小到6.71%,建模精度有瞭較大的提高。結果錶明,採取經驗模態分解的算法進行光電採集數據的去譟預處理可以提高光譜數據的信譟比,進而可以提高血液成分無創測量的準確性。
장경험모태분해(EMD)산법결합동태광보이론중적빈역제취산법용우혈홍단백농도적무창측량。재체채집57례광전용적맥박파,선취636.98~1086.86 nm범위내적광보수거진행분석。수선통과EMD방법분별대각개양본매개파장적광전용적맥박파진행거조예처리,재이용리산부리협변환제취맥박파적봉봉치구성동태광보,최후운용편최소이승방법대각양본적동태광보화혈홍단백농도건립모형。여미경EMD처리적수거건모결과상비,EMD처리후,혈홍단백농도예측집적상관계수종0.8798제고도0.9176,예측집균방근오차종6.6759 g·L-1감소도5.3001 g·L-1,상대오차종8.45%감소도6.71%,건모정도유료교대적제고。결과표명,채취경험모태분해적산법진행광전채집수거적거조예처리가이제고광보수거적신조비,진이가이제고혈액성분무창측량적준학성。
Empirical mode decomposition (EMD)algorithm combined with the theory of dynamic spectrum extraction at frequen-cy domain was applied to the noninvasive measurement of hemoglobin concentration.Fifty seven cases’photoplethysmography was collected in the range of 636. 98~1 086. 86 nm in vivo.After the denoising preprocess through the EMD method for each wavelength pulse wave of each sample separately,dynamic spectrum of each sample was made up of all peaks extracted by Fou-rier transform.Partial least squares regression model was used to establish the calibration and prediction of hemoglobin concen-tration.Compared to the modeling results without EMD,the correlation coefficient of predicted values and the real values was increased from 0. 879 8 up to 0. 917 6.The root mean square error of prediction set was reduced from 6. 675 9 to 5. 300 1 g·L-1 and the relative error was reduced from 8. 45% to 6. 71%.The modeling accuracy has been greatly improved.The results showed that EMD algorithm can be effectively applied to denoise the spectral data and improve the accuracy of the non-invasive measurement of blood components.