Near-infrared parameters extraction: A potential method to detect skin cancer

The wavelength-dependent absorption coefficients can be used to analyse optical properties of human skin. Existing absorption models for narrow ranges in the visible and near infrared are insufficient to simultaneously incorporate the spectral contrast produced by differences in chromophores, water and lipid content of skin tissue into skin cancer detection. In the broad range up to 1600 nm, recent analysis approaches for absorption spectra do not consistently provide significant differences between healthy and cancerous skins. We propose an absorption model to fit the absorption coefficient spectra of skin samples over the range from 400 nm to 1600 nm and an advanced algorithm to find the optimal estimation. The extracted parameters of this model are analysed by a statistical t-test. The test results demonstrate the significant differences between all pairs of tumour-normal skin. Therefore, our approach has strong potential for early skin cancer detection using near infrared spectroscopy (NIRS). © 2013 IEEE

High correlation of double Debye model parameters in skin cancer detection

© 2014 IEEE. The double Debye model can be used to capture the dielectric response of human skin in terahertz regime due to high water content in the tissue. The increased water proportion is widely considered as a biomarker of carcinogenesis, which gives rise of using this model in skin cancer detection. Therefore, the goal of this paper is to provide a specific analysis of the double Debye parameters in terms of non-melanoma skin cancer classification. Pearson correlation is applied to investigate the sensitivity of these parameters and their combinations to the variation in tumor percentage of skin samples. The most sensitive parameters are then assessed by using the receiver operating characteristic (ROC) plot to confirm their potential of classifying tumor from normal skin. Our positive outcomes support further steps to clinical application of terahertz imaging in skin cancer delineation

Breast Cancer classification using extracted parameters from a terahertz dielectric model of human breast tissue

© 2015 IEEE. Our previous study proposed a dielectric model for human breast tissue and provided initial analysis of classification potential of the eight model parameters and their multiparameter combinations with the support vector machine (SVM). A combination of three model parameters could achieve a leave-one-out cross validation accuracy of 93.2%. However, the SVM approach fails to exploit the combinations of more than three model parameters for classification improvement. Thus, the Bayesian neural network (BNN) method is employed to overcome this problem based on its advantages of handling our small data and high complexity of the multiparamter combinations. The BNN successfully classifies the data using the combinations of four model parameters with an accuracy, estimated by leave-one-out cross validation, of 97.3%. Overall performance assessed by leaveone- out and repeated random-subsampling cross validations for all examined combinations is also remarkably improved by BNN. The results indicate the advance of BNN as compared to SVM in utilising the model parameters for detecting tumour from normal breast tissue

Debye parameter extraction for characterizing interaction of terahertz radiation with human skin tissue

This paper is concerned with parameter extraction for the double Debye model, which is used for analytically determining human skin permittivity. These parameters are thought to be the origin of contrast in terahertz (THz) images of skin cancer. The existing extraction methods could generate Debye models, which track their measurements accurately at frequencies higher than 1 THz but poorly at lower frequencies, where the majority of permittivity contrast between healthy and diseased skin tissues is actually observed. We propose a global optimization-based parameter extraction, which results in globally accurate tracking and thus supports the full validity of the Debye model for simulating human skin permittivity in the whole usable THz frequencies. Numerical results confirm viability of our novel methodology. © 1964-2012 IEEE

Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia

© 2018 IEEE. Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly (P < 0.0001) while centroid theta increased considerably (P < 0.01). Spectral entropy of the unified theta-alpha band rose significantly (P < 0.005). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively

System identification for Terahertz wave's propagation and reflection in human skin

This paper is concerned with parameter identification for the double Debye model of the Terahertz wave's propagation and reflection in human skin. The existing methods could provide estimators, which are accurate at the frequencies higher than one THz but rather row at the lower frequencies, where the majority of contrast for differentiating the changes of skin content is present. We propose another approach by using parametric quadratic optimization to locate the global optimal estimator. Simulation results confirm our reliable and prominent technique. © 2012 IEEE

Global optimization for human skin investigation in terahertz

In this paper, the electromagnetic interaction between human skin and terahertz radiation is investigated through the double Debye parameters' extraction algorithm. The changes of skin content are contrasted at the frequencies below one terahertz(THz) but the recent approaches could provide only a rough estimation. We propose an global optimization based identification, which results in globally accurate estimators in the frequency range up to two THz, and thus supports the validity of Debye model for Terahertz wave's propagation and reflection in skin. Simulation results confirm our prominent methodology. © 2012 IEEE

A dielectric model of human breast tissue in terahertz regime

© 2014 IEEE. The double Debye model has been used to understand the dielectric response of different types of biological tissues at terahertz (THz) frequencies but fails in accurately simulating human breast tissue. This leads to limited knowledge about the structure, dynamics, and macroscopic behavior of breast tissue, and hence, constrains the potential of THz imaging in breast cancer detection. The first goal of this paper is to propose a new dielectric model capable of mimicking the spectra of human breast tissue's complex permittivity in THz regime. Namely, a non-Debye relaxation model is combined with a single Debye model to produce a mixture model of human breast tissue. A sampling gradient algorithm of nonsmooth optimization is applied to locate the optimal fitting solution. Samples of healthy breast tissue and breast tumor are used in the simulation to evaluate the effectiveness of the proposed model. Our simulation demonstrates exceptional fitting quality in all cases. The second goal is to confirm the potential of using the parameters of the proposed dielectric model to distinguish breast tumor from healthy breast tissue, especially fibrous tissue. Statistical measures are employed to analyze the discrimination capability of the model parameters while support vector machines are applied to assess the possibility of using the combinations of these parameters for higher classification accuracy. The obtained analysis confirms the classification potential of these features

The Potential of the Double Debye Parameters to Discriminate between Basal Cell Carcinoma and Normal Skin

� 2015 IEEE. The potential of terahertz imaging for improving the efficiency of Mohs's micrographic surgery in terms of tumor margin detection was previously studied. Thanks to high water content of human skin, its dielectric response to terahertz radiation can be described by the double Debye model which uses five parameters to fit experimental data. Skin tumors typically have a higher water content than normal tissues do, and this should be apparent in the parameters. The goal of this paper is to apply statistical methods to these parameters to test their power to differentiate skin cancer from normal tissue. Based on the prediction accuracy estimated using a cross-validation method, we found the best classifier was the static permittivity at low frequency (εs). By combining the most relevant parameters, we obtained a classification accuracy of 95.7%, confirming the classification capability of the parameters, thereby supporting their application to improve terahertz imaging for the purpose of skin cancer delineation