Statistical Curve Analysis: Developing Methods and Expanding Knowledge in Health

The analysis of curves can be claimed to be the core of most scientific ventures. In this dissertation, we focus on the statistical aspect of this type of analysis. Here, the curves originate from health and food-related areas and include improvements in blood glucose measurements, classification of moles, measurements of parameters during liver transplants in pigs, and data from the monitoring of the quality of fish. More specifically, the statistical curve analysis consists of several perspectives were all have some kind of in- trinsic comparison effort. However, the main approaches in these studies are related to regression and the problem of finding suitable critical regions. The regression part consists of robust nonlinear regression and linear mixed models while the critical regions are found through classification and hypothesis testing in scale-space. By improving the critical decision boundaries through e.g. the Bonferroni correction of scale-space maps in Paper I, and developing features to improve decisions regarding the classification of moles in Paper II, we were able to obtain high sensitivity and specificity in the developed systems. Re- gression was an integral part of the classification effort in Paper II, the improvement of blood glucose measurements in Paper III, and the statistical analysis of parameters measured during liver transplantation in pigs in Paper IV. Paper I is focused on maximizing sensitivity and specificity when detecting a significant change in the data. Here as in Paper II hyperspectral images are the source of data. The developed method produces a scale-space, where significant changes can be detected. Paper II aims to maximize sensitivity, specificity, and precision in the classification of moles. This is accomplished through curves from subimages obtained from each channel of the hyperspectral images. These curves show characteristic features from three important classes of moles. By using these features through the regression of these curves, we accomplish high sensitivity, specificity, and precision in the classification pursuit. In Paper III, we introduce a novel method for improving blood glucose estimation from continuous glucose measurements by using deconvolution. First, regression is used to estimate the parameters in the convolution kernel. Thereafter this response function was deconvolved through regression. In this way, we can estimate blood glucose from subcutaneous measurements. This gives a new method for controlling blood glucose levels which is of great importance for type 1 diabetes patients during and after exercise to avoid hypoglycemia. Testing two different methods in liver transplantation of pigs, where the statistical analysis of curves was done through the application of linear mixed models, is the focus of Paper IV. An important output of this work is that the two treatments can be statistically distinguished through the use of linear mixed models

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI

Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies can be very useful in such situations since exploring the measurements in multiple resolutions can help identify even subtle changes. We extend a recently proposed scale-space methodology to a technique that successfully detects such small changes and at the same time keeps false alarms at a very low level. The potential of the novel methodology is first demonstrated with hyperspectral skin mole data artificially distorted to include a very small change. Our real data application considers hyperspectral images used for food quality detection. In these experiments the performance of the proposed method is either superior or on par with a standard approach such as principal component analysis

Statistical Curve Analysis: Developing Methods and Expanding Knowledge in Health

The analysis of curves can be claimed to be the core of most scientific ventures. In this dissertation, we focus on the statistical aspect of this type of analysis. Here, the curves originate from health and food-related areas and include improvements in blood glucose measurements, classification of moles, measurements of parameters during liver transplants in pigs, and data from the monitoring of the quality of fish. More specifically, the statistical curve analysis consists of several perspectives were all have some kind of in- trinsic comparison effort. However, the main approaches in these studies are related to regression and the problem of finding suitable critical regions. The regression part consists of robust nonlinear regression and linear mixed models while the critical regions are found through classification and hypothesis testing in scale-space. By improving the critical decision boundaries through e.g. the Bonferroni correction of scale-space maps in Paper I, and developing features to improve decisions regarding the classification of moles in Paper II, we were able to obtain high sensitivity and specificity in the developed systems. Re- gression was an integral part of the classification effort in Paper II, the improvement of blood glucose measurements in Paper III, and the statistical analysis of parameters measured during liver transplantation in pigs in Paper IV. Paper I is focused on maximizing sensitivity and specificity when detecting a significant change in the data. Here as in Paper II hyperspectral images are the source of data. The developed method produces a scale-space, where significant changes can be detected. Paper II aims to maximize sensitivity, specificity, and precision in the classification of moles. This is accomplished through curves from subimages obtained from each channel of the hyperspectral images. These curves show characteristic features from three important classes of moles. By using these features through the regression of these curves, we accomplish high sensitivity, specificity, and precision in the classification pursuit. In Paper III, we introduce a novel method for improving blood glucose estimation from continuous glucose measurements by using deconvolution. First, regression is used to estimate the parameters in the convolution kernel. Thereafter this response function was deconvolved through regression. In this way, we can estimate blood glucose from subcutaneous measurements. This gives a new method for controlling blood glucose levels which is of great importance for type 1 diabetes patients during and after exercise to avoid hypoglycemia. Testing two different methods in liver transplantation of pigs, where the statistical analysis of curves was done through the application of linear mixed models, is the focus of Paper IV. An important output of this work is that the two treatments can be statistically distinguished through the use of linear mixed models

Estimation of Blood Glucose Concentration During Endurance Sports

In this paper, we describe a new statistical approach to estimate blood glucose concentration along time during endurance sports based on measurements of glucose concentration in subcutaneous interstitial tissue. The final goal is the monitoring of glucose concentration in blood to maximize performance in endurance sports. Blood glucose concentration control during and after aerobic physical activity could also be useful to reduce the risk of hypoglycemia in type 1 diabetes mellitus subjects. By means of a low invasive technology known as "continuous glucose monitoring", glucose concentration in subcutaneous interstitial tissue can now be measured every five minutes. However, it can be expressed as function of blood glucose concentration along time by means of a convolution integral equation. In the training phase of the proposed approach, based on measurements of glucose concentration in both artery and subcutaneous interstitial tissue during physical activity, the parameters of the convolution kernel are estimated. Then, given a new subject performing aerobic physical activity, a deconvolution problem is solved to estimate glucose concentration in blood from continuous glucose monitoring measurements

Investigation of STEM Subject and Career Aspirations of Lower Secondary School Students in the North Calotte Region of Finland, Norway, and Russia

This study investigates the suitability of the STEM Career Interest Survey (STEM-CIS) to measure secondary school students’ aspirations towards STEM subjects and careers. A confirmatory factor analysis (CFA) was conducted to assess the initial structural validity of the adapted STEM-CIS survey, where the science subscale was extended to four science disciplines, to align with the way science is taught in Finland and Russia. The results indicate that the interest in STEM subjects in general is not at a high level in any of the countries. There is a traditional gender gap regarding STEM subjects in every dimension, which favors females in biology and males in technology and engineering. STEM stereotypes among students—due to low exposure to STEM professions at school—can explain students’ low interest despite high self-efficacies. Our study shows that we must increase informal learning opportunities inside and outside school and improve career counselling for students so that they will be more informed of STEM career opportunities

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI

Investigation of STEM subject and career aspirations of lower secondary school students in the North Calotte region of Finland, Norway, and Russia

Abstract This study investigates the suitability of the STEM Career Interest Survey (STEM-CIS) to measure secondary school students’ aspirations towards STEM subjects and careers. A confirmatory factor analysis (CFA) was conducted to assess the initial structural validity of the adapted STEM-CIS survey, where the science subscale was extended to four science disciplines, to align with the way science is taught in Finland and Russia. The results indicate that the interest in STEM subjects in general is not at a high level in any of the countries. There is a traditional gender gap regarding STEM subjects in every dimension, which favors females in biology and males in technology and engineering. STEM stereotypes among students—due to low exposure to STEM professions at school—can explain students’ low interest despite high self-efficacies. Our study shows that we must increase informal learning opportunities inside and outside school and improve career counselling for students so that they will be more informed of STEM career opportunities

The three-factor model:a study of common features in students’ attitudes towards studying and learning science and mathematics in the three countries of the North Calotte region

Abstract This study investigated common features of students’ attitudes towards studying science and mathematics in comprehensive and secondary schools in three countries. Data were obtained by conducting a survey (N = 581) in Norway, Finland and Russia. A Confirmatory factor analysis (CFA) provided a model with a three-factor solution consisting of factors: the perception of the teacher, anxiety towards science and mathematics, and motivation. The results suggest that most students are motivated to study sciences and mathematics. Data analysis indicate gender differences in attitudes to students’ future studies and career plans. Most girls recognized the importance of these subjects for their future studies and careers, while boys showed more interest than girls in local career opportunities in industry. Teachers have a significant role in directing students’ attitudes toward science and mathematics. Students experienced that the teachers who use innovative teaching approaches, both motivate and reduce anxiety, in their learning process

Early detection of change by applying scale-space methodology to hyperspectral images

Abstract Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies can be very useful in such situations since exploring the measurements in multiple resolutions can help identify even subtle changes. We extend a recently proposed scale-space methodology to a technique that successfully detects such small changes and at the same time keeps false alarms at a very low level. The potential of the novel methodology is first demonstrated with hyperspectral skin mole data artificially distorted to include a very small change. Our real data application considers hyperspectral images used for food quality detection. In these experiments the performance of the proposed method is either superior or on par with a standard approach such as principal component analysis