Vital Sign Signals (VSSs) have been widely used for medical data analysis. One classic approach is to use Logistic Regression Model (LRM) to describe data to be analyzed. There are two challenging issues from this approach. One is how many VSSs needed to be used in the model since there are many VSSs can be used for this purpose. Another is that once the number of VSSs is determined, the follow-up issue what these VSSs are. Up to date these two issues are resolved by empirical selection. This paper addresses these two issues from a hyperspectral imaging perspective. If we view a patient with collected different vital sign signals as a pixel vector in hyperspectral image, then each vital sign signal can be considered as a particular band. In light of this interpretation each VSS can be ranked by band prioritization commonly used by band selection in hyperspectral imaging. In order to resolve the issue of how many VSSs should be used for data analysis we further develop a Progressive Band Processing of Anomaly Detection (PBPAD) which allows users to detect anomalies in medical data using prioritized VSSs one after another so that data changes between bands can be dictated by profiles provided by PBPAD. As a result, there is no need of determining the number of VSSs as well as which VSS should be used because all VSSs are used in their prioritized orders. To demonstrate the utility of PBPAD in medical data analysis anomaly detection is implemented as PBP to find anomalies which correspond to abnormal patients. The data to be used for experiments are data collected in University of Maryland, School of Medicine, Shock Trauma Center (STC). The results will be evaluated by the results obtained by Logistic Regression Model (LRM).
When medical data are collected there are many Vital Sign Signals (VSSs) that can be used for data analysis. From a hyperspectral imaging perspective, we can consider a patient with different vital sign signals as a pixel vector in hyperspectral image and each vital sign signal as a particular band. In light of this interpretation this paper develops two new concepts of prioritization of VSSs. One is Orthogonal Subspace Projection Residual (OSPR), which measures the residual of a VSS in the orthogonal complement subspace to the space linearly spanned by the remaining VSSs. Another is to construct a histogram for each of VSSs that can be used as a means of ranking VSSs according to a certain criterion for optimality. Several measures are proposed to be used as criteria for VSS prioritization, which are variance, entropy and Kullbak-Leibler (KL) information measure. VSS prioritization can then be used as the VSS selection method to form Logistic Regression model (LRM). In order to determine how many VSSs should be used a recently developed concept, called Virtual Dimensionality (VD) can be used for this purpose. To demonstrate the utility of VSS prioritization, data collected in University of Maryland, School of Medicine, Shock Trauma Center (STC) was used for experiments.
Virtual dimensionality (VD) has been widely used to estimate number of endmembers in the past. Unfortunately, the original idea of VD was developed to specify the number of spectrally distinct signatures in hyperspectral data where there is no provided specific definition of what “spectrally distinct signatures” are. As a result, many techniques developed to estimate VD have produced various values for VD. This paper addresses this issue by develops a target specified VD (TSVD) theory where the value of VD is completely determined by targets of interest. In particular, the VD techniques can be categorized according to targets characterized by eigenvalues/eigenvectors and real target signal sources which are used for a binary composite hypothesis testing problem. For the latter case the Automatic Target Generation Process (ATGP) is particularly used to generate real target signal sources to replace eigenvalues/eigenvectors as signal sources to be used for the binary hypothesis testing problem. In order to find probability distributions under each hypothesis the extreme theory used by Maximum Orthogonal Complement Algorithm (MOCA) is used for their derivations. As a result, VD can be estimated by two types of signals sources, eigenvalues/eigenvectors along with two types of detectors, maximum likelihood detector and Neyman-Pearson detector.