NDLI: The combination of Self-Organizing Feature Maps and support vector regression for solving the inverse ECG problem

Content Provider	IEEE Xplore Digital Library
Author	Mingfeng Jiang Jiafu Lv Shanshan Jiang Wenqing Huang Li Cao
Copyright Year	2012
Description	Author affiliation: The School of Electronics and Informatics, Zhejiang Sci-Tech University, Hangzhou, China (Mingfeng Jiang; Jiafu Lv; Shanshan Jiang; Wenqing Huang; Li Cao)
Abstract	Compared to body surface potentials (BSPs) recordings, myocardial transmembrane potentials (TMPs) can provide more detailed and complicated electrophysiological information. So the reconstruction of TMPs is regarded as a promising way for the diagnosis of cardiac diseases. This paper proposed the hybrid method of SVR with the Self-Organizing Feature Map (SOFM) technique to lessen training time and to improve the reconstruction accuracies. The model was implemented by the following processes: SOFM algorithm was adopted to cluster the training samples; and the individual SVR model for each cluster was then constructed. For each testing sample, find the cluster to which it belongs, and then use the corresponding SVR model to reconstruct the TMPs. The proposed model was tested and compared with single SVR schemes using a realistic heart-torso model. The experiment results show that the proposed SOFM-SVR is an improvement over the traditional single SVR in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs.
Starting Page	475
Ending Page	479
File Size	423070
Page Count	5
File Format	PDF
ISBN	9781457721304
ISSN	21579563
e-ISBN	9781457721335
DOI	10.1109/ICNC.2012.6234692
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines transmembrane potentials (TMPs) Inverse ECG Neurons Training data Support Vector Regression Predictive models Self-Organizing Feature Map Data models Testing
Content Type	Text
Resource Type	Article

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