Applications of Statistical Models for Hemoglobin Spectral Signatures
Date Issued
2013
Date
2013
Author(s)
Wong, Li-Shan
Abstract
The research and applications of optical spectroscopy techniques in medical diagnoses and therapies have advanced greatly in recent years. It has been known that organic compounds have their own unique spectrum signature, and this uniqueness property can be used to perform differentiation. In particular, the change in the oxygen saturation state of hemoglobin plays an important role in the development of many diseases, including early cancer and precancerous lesion performances in humans. Several studies have shown that the visible spectrum signatures of deoxy-hemoglobin and oxy-hemoglobin absorption spectra are different. Unfortunately, not many statistical models have been applied in the analysis of such data. The aim of this study is to construct statistical models to differentiate deoxy-hemoglobin from oxy-hemoglobin based on a probabilistic prediction of optical spectral signatures.
In this study, we first constructed two statistical models for the deoxy-hemoglobin and oxy-hemoglobin absorption spectra signatures, respectively, and then used the models to differentiate the existence of deoxy-hemoglobin and oxy-hemoglobin absorption spectra by their signatures. These models contained random effects to describe the variation between observations and correlation among repeated measurements taken from the same subject. To examine the performance of these models in response to the variation, we simulated deoxy-hemoglobin and oxy-hemoglobin datasets under different variances of random effects and intensities. The datasets were generated based on the optical absorption spectra of hemoglobin observations on websites. After hemoglobin datasets were generated, the predictive accuracies of two statistical models were assessed with cross validation. The results showed that, for deoxy-hemoglobin model, the average of sensitivity was between 96% and 97% and average of false positive rate was between 8% and 17%. On the other hand, sensitivity of oxy-hemoglobin model was between 95% and 96% and false positive rate between 26% and 54%. In general, these two models differentiate well the deoxy-hemoglobin from oxy-hemoglobin spectrum data.
Some issues and limitations are worth mentioning. First, either the deoxy-hemoglobin or the oxy-hemoglobin spectrum signatures for training and testing data were simulated based on a single data set on web, it is likely that the applicability of the models would be limited. Therefore, the applications on other data from experiments are needed. In addition, due to the heterogeneity among individuals, the repeated measurements on more individuals would help to improve the estimates and prediction of the models. Third, if both deoxy-hemoglobin and oxy-hemoglobin spectrum signatures can be collected from the same individual, then a unify model containing characteristics of both signatures can be developed and a contrast variable can be included in the model. Future research can extend our statistical model considered here to have a better performance in the inference of observations from deoxy-hemoglobin and oxy-hemoglobin spectrum.
Subjects
血紅素
線性模式
可見光吸收光譜
SDGs
Type
thesis
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