Four Types of Pulse Oximeters Accurately Detect Hypoxia during Low Perfusion and Motion

2018 ◽  
Vol 128 (3) ◽  
pp. 520-530 ◽  
Author(s):  
Aaron Louie ◽  
John R. Feiner ◽  
Philip E. Bickler ◽  
Laura Rhodes ◽  
Michael Bernstein ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Oxygen Saturation ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Pulse Oximeter ◽  
Motion Artifacts ◽  
Arterial Oxygen ◽  
Mean Square ◽  
Blood Samples ◽  
Pulse Oximeters

Abstract Background Pulse oximeter performance is degraded by motion artifacts and low perfusion. Manufacturers developed algorithms to improve instrument performance during these challenges. There have been no independent comparisons of these devices. Methods We evaluated the performance of four pulse oximeters (Masimo Radical-7, USA; Nihon Kohden OxyPal Neo, Japan; Nellcor N-600, USA; and Philips Intellivue MP5, USA) in 10 healthy adult volunteers. Three motions were evaluated: tapping, pseudorandom, and volunteer-generated rubbing, adjusted to produce photoplethsmogram disturbance similar to arterial pulsation amplitude. During motion, inspired gases were adjusted to achieve stable target plateaus of arterial oxygen saturation (SaO2) at 75%, 88%, and 100%. Pulse oximeter readings were compared with simultaneous arterial blood samples to calculate bias (oxygen saturation measured by pulse oximetry [SpO2] − SaO2), mean, SD, 95% limits of agreement, and root mean square error. Receiver operating characteristic curves were determined to detect mild (SaO2 < 90%) and severe (SaO2 < 80%) hypoxemia. Results Pulse oximeter readings corresponding to 190 blood samples were analyzed. All oximeters detected hypoxia but motion and low perfusion degraded performance. Three of four oximeters (Masimo, Nellcor, and Philips) had root mean square error greater than 3% for SaO2 70 to 100% during any motion, compared to a root mean square error of 1.8% for the stationary control. A low perfusion index increased error. Conclusions All oximeters detected hypoxemia during motion and low-perfusion conditions, but motion impaired performance at all ranges, with less accuracy at lower SaO2. Lower perfusion degraded performance in all but the Nihon Kohden instrument. We conclude that different types of pulse oximeters can be similarly effective in preserving sensitivity to clinically relevant hypoxia.

Evaluation of a New Pulse Oximeter Sensor

2007 ◽  
Vol 16 (2) ◽  
pp. 146-152 ◽  
Author(s):  
Marco Fernandez ◽  
Kathy Burns ◽  
Beverly Calhoun ◽  
Saramma George ◽  
Beverly Martin ◽  
...  
Keyword(s):  
Cardiac Index ◽  
Oxygen Saturation ◽  
Blood Sample ◽  
Root Mean Square ◽  
Arterial Oxygen Saturation ◽  
Method Comparison ◽  
Arterial Oxygen ◽  
Mean Square ◽  
Level Of Significance ◽  
Pulse Oximeters

• Background A new forehead noninvasive oxygen saturation sensor may improve signal quality in patients with low cardiac index. • Objectives To examine agreement between oxygen saturation values obtained by using digit-based and forehead pulse oximeters with arterial oxygen saturation in patients with low cardiac index. • MethodsA method-comparison study was used to examine the agreement between 2 different pulse oximeters and arterial oxygen saturation in patients with low cardiac index. Readings were obtained from a finger and a forehead sensor and by analysis of a blood sample. Bias, precision, and root mean square differences were calculated for the digit and forehead sensors. Differences in bias and precision between the 2 noninvasive devices were evaluated with a t test (level of significance P<.05). • Results Nineteen patients with low cardiac index (calculated as cardiac output in liters per minute divided by body surface area in square meters; mean 1.98, SD 0.34) were studied for a total of 54 sampling periods. Mean (SD) oxygen saturations were 97% (2.4) for blood samples, 96% (3.2) for the finger sensor, and 97% (2.8) for the forehead sensor. By Bland Altman analysis, bias ± precision was −1.16 ± 1.62% for the digit sensor and −0.36 ± 1.74% for the forehead sensor; root mean square differences were 1.93% and 1.70%, respectively. Bias and precision differed significantly between the 2 devices; the forehead sensor differed less from the blood sample. • Conclusions In patients with low cardiac index, the forehead sensor was better than the digit sensor for pulse oximetry.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Analysis of candlenut oil as oil adulterant in three functional oils of soybean oil, sunflower oil and grapeseed oil in quaternary mixture systems using FTIR spectroscopy and chemometrics

Food Research ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 248-253
Author(s):  
A.B. Riyanta ◽  
S. Riyanto ◽  
E. Lukitaningsih ◽  
A. Rohman
Keyword(s):  
Root Mean Square Error ◽  
Soybean Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sunflower Oil ◽  
Ftir Spectra ◽  
Mean Square ◽  
Grapeseed Oil ◽  
Quaternary Mixture

Soybean oil (SBO), sunflower oil (SFO) and grapeseed oil (GPO) contain high levels of unsaturated fats that are good for health and have proximity to candlenut oil. Candlenut oil (CNO) has a lower price and easier to get oil from that seeds than other seed oils, so it is used as adulteration for gains. Therefore, authentication is required to ensure the purity of oils by proper analysis. This research was aimed to highlight the FTIR spectroscopy application with multivariate calibration is a potential analysis for scanning the quaternary mixture of CNO, SBO, SFO and GPO. CNO quantification was performed using multivariate calibrations of principle component (PCR) regression and partial least (PLS) square to predict the model from the optimization FTIR spectra regions. The highest R2 and the lowest values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were used as the basis for selection of multivariate calibrations created using several wavenumbers region of FTIR spectra. Wavenumbers regions of 4000-650 cm-1 from the second derivative FTIR-ATR spectra using PLS was used for quantitative analysis of CNO in quaternary mixture with SBO, SFO and GPO with R2 calibration = 0.9942 and 0.0239% for RMSEC value and 0.0495%. So, it can be concluded the use of FTIR spectra combination with PLS is accurate to detect quaternary mixtures of CNO, SBO, SFO and GPO with the highest R2 values and the lowest RMSEC and RMSEP values.

scholarly journals A useful quantitative model for determination of enantiomeric composition of racemate praziquantel by ultraviolet spectroscopy combined with partial least squares and its application to praziquantel tablets

2018 ◽  
Vol 11 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Man Zhao ◽  
Ran Meng ◽  
Yifang Lu ◽  
Lingyun Hu ◽  
Na Sun ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Reference Value ◽  
Quantitative Model ◽  
Enantiomeric Composition ◽  
Mean Square ◽  
Validation Set

A simple and novel method has been proposed to determine the enantiomeric composition of racemate praziquantel (PZQ) by using the analysis of ultraviolet (UV) spectroscopy combined with partial least squares (PLS). This method does not rely on the use of expensive carbohydrates such as cyclodextrins, but on the use of inexpensive sucrose, which is equally effective as carbohydrate. PZQ has two enantiomers. Through measuring the slight difference in the UV spectral absorption of PZQ due to different interactions between its two enantiomers and sucrose, the enantiomeric composition was determined by a quantitative model based on PLS analysis. The model showed that the correlation coefficients of calibration set and validation set were 0.9971 and 0.9972, respectively. The root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) were 0.0167 and 0.0129, respectively. Then, the independent data of PZQ tablets were also used to test how well the quantitative model of PLS predicted the enantiomeric composition. The ratio of S-PZQ in tablet was 0.492, determined by high-performance liquid chromatography as the reference value. Six solutions of the tablet samples were prepared, and the ratios of S-PZQ in tablet samples in the validation set were predicted by the PLS model. Their relative errors with the reference value were not more than 4%. Therefore, the established model could be accurate and employed to predict the enantiomeric compositions of PZQ tablets.

scholarly journals Efficient Market Hypothesis and the RMB-Dollar Rates: A Nonlinear Modeling of the Exchange Rate

2018 ◽  
Vol 10 (2) ◽  
pp. 150
Author(s):  
Hongxing Yao ◽  
Abdul Rashid Abdul Rahaman
Keyword(s):  
Exchange Rate ◽  
Root Mean Square Error ◽  
Linear Model ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Nonlinear Model ◽  
Efficient Market Hypothesis ◽  
Mean Square ◽  
Forecast Performance ◽  
The Exchange Rate

This paper uses a SETAR model to determine threshold(s) in the RMB/US$ exchange rate from 1981 to 2016 using monthly data. Also, it compares the forecast performance of the univariate nonlinear model to a univariate linear model. We further analyze the forecast performance of the SETAR model to a multivariate linear model, e.g., a Reduced-form VAR. In addition, the research assesses the claim by Boero and Marrocu (2002) that the root mean square error masks the superiority of the nonlinear models.We found five significant thresholds in the RMB/US$ exchange rate, and this result reflects five major episodes of policy reforms or structural changes in the renminbi exchange rates from the period 1981 to 2016. We also found that the univariate nonlinear model out performs both the univariate and multivariate linear models in predicting the exchange rate movements. This finding is consistent with the results in Kyei and Gyamfi (2016), Boero and Marrocu (2002), Krager and Kugler (1993), Peel and Speight (1994) and Chappell et al. (1996). Furthermore, we did not find any evidence of the root mean square error masking the superiority of the nonlinear model.

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