Name
Title | ||
|---|---|---|
Time Series Mining Approach for Noninvasive Intracranial Pressure Assessment: An Investigation of Different Regularization Techniques |
Abstract | ||
|---|---|---|
A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function for each entry in the database. In this paper, three different mapping function solutions, linear least squares (LLS), truncated singular value decomposition (TSVD) and standard Tikhonov regularization (STR) are systemically tested to compare the possible effects of different solutions on the non-invasive ICP estimation. The conducted comparison demonstrated that the selection of mapping function solution actually influences the estimation. Among the tested three solutions for mapping function, TSVD and STR show better ICP estimation performance with smaller ICP errors than LLS. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSIE.2009.861 | CSIE (5) |
Keywords | Field | DocType |
standard tikhonov regularization,time series mining,feature vector,orthopaedics,flow velocity,least square,different regularization techniques,noninvasive icp estimation,singular value decomposition,blood vessels,noninvasive intracranial pressure,mapping function solution,str show,non-invasive icp estimation,least squares approximations,truncated singular value decomposition,arterial blood pressure,time series mining approach,regularization technique,different solution,data mining,smaller icp error,noninvasive intracranial pressure assessment,data mining framework,icp estimation performance,medical computing,linear least squares,different mapping function solution,mapping function,intracranial pressure,time series,patient treatment,tikhonov regularization,time series analysis,databases,estimation,robustness | Least squares,Tikhonov regularization,Data mining,Time series,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Linear least squares,Computer vision,Singular value decomposition,Feature vector,Algorithm,Mathematics | Conference |
Volume | ISBN | Citations |
5 | 978-0-7695-3507-4 | 1 |
PageRank | References | Authors |
0.37 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaozhi Wu | 1 | 6 | 1.07 |
| Peng Xu | 2 | 75 | 15.22 |
| Shadnaz Asgari | 3 | 111 | 11.02 |
| Marvin Bergsneider | 4 | 67 | 10.75 |
| Xiao Hu | 5 | 72 | 13.64 |