Abstract
Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency electrical impedance data within real-time RFA depth estimation through data fusion schemes that include non-linear machine learning (ML) models. Multi-frequency tissue complex electrical impedance measurements are used to provide input data to the data fusion schemes. Our results show that the fusion schemes significantly decrease both the spread of residuals and the mean of the residuals for depth estimation. Thus, data fusion can be a significant tool for use in improving the performance of ML-based monitoring for RFA.
| Original language | English (US) |
|---|---|
| Article number | 8895991 |
| Pages (from-to) | 2359-2367 |
| Number of pages | 9 |
| Journal | IEEE Journal of Biomedical and Health Informatics |
| Volume | 24 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2020 |
Funding
Manuscript received July 24, 2019; revised November 1, 2019; accepted November 4, 2019. Date of publication November 11, 2019; date of current version August 5, 2020. This work was supported by National Science Foundation under Grants 1622842 and 1738541. (Corresponding author: Alan V. Sahakian.) E. Besler is with the Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208 USA (e-mail: [email protected]).
Keywords
- Data fusion
- adaptive boosting
- cancer
- control
- depth
- ensemble
- lesion
- machine learning
- monitoring
- radiofrequency ablation
- random forest
- svm
- tumor
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics
- Electrical and Electronic Engineering
- Health Information Management
