A Clustering-based approach for prediction of resynchronization therapy

Heng Huang, Li Shen, Fillia Makedon, Sheng Zhang, Mark Greenberg, Ling Gao, Justin Pearlman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper presents a method for predicting pacing sites in the left ventricle of a heart and its result can be used to assist device programming in cardiac resynchronization therapy (CRT), which is a widely adopted therapy for heart failure patients. In a traditional CRT device deployment, pacing sites are selected without quantitative prediction. That runs the risk of suboptimal benefits. In this work, a surface tracking method is proposed to describe the ventricular wall motion and a hierarchical agglomerative clustering technique is applied to radial motion series to find candidate pacing sites. Using clinical MRI data in our experiments, we show that the proposed method performs as well as we expect. Our approach can not only effectively identify suitable pacing sites, but also distinguish patients from normals perfectly to help medical diagnosis.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM Symposium on Applied Computing
Pages260-266
Number of pages7
Volume1
StatePublished - 2005
Externally publishedYes
Event20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States
Duration: Mar 13 2005Mar 17 2005

Other

Other20th Annual ACM Symposium on Applied Computing
CountryUnited States
CitySanta Fe, NM
Period3/13/053/17/05

Fingerprint

Cardiac resynchronization therapy
Magnetic resonance imaging
Experiments

Keywords

  • Cardiac resynchronization therapy
  • Clustering
  • Computer assisted diagnosis and prognosis
  • Data mining

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Huang, H., Shen, L., Makedon, F., Zhang, S., Greenberg, M., Gao, L., & Pearlman, J. (2005). A Clustering-based approach for prediction of resynchronization therapy. In Proceedings of the ACM Symposium on Applied Computing (Vol. 1, pp. 260-266)

A Clustering-based approach for prediction of resynchronization therapy. / Huang, Heng; Shen, Li; Makedon, Fillia; Zhang, Sheng; Greenberg, Mark; Gao, Ling; Pearlman, Justin.

Proceedings of the ACM Symposium on Applied Computing. Vol. 1 2005. p. 260-266.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Huang, H, Shen, L, Makedon, F, Zhang, S, Greenberg, M, Gao, L & Pearlman, J 2005, A Clustering-based approach for prediction of resynchronization therapy. in Proceedings of the ACM Symposium on Applied Computing. vol. 1, pp. 260-266, 20th Annual ACM Symposium on Applied Computing, Santa Fe, NM, United States, 3/13/05.
Huang H, Shen L, Makedon F, Zhang S, Greenberg M, Gao L et al. A Clustering-based approach for prediction of resynchronization therapy. In Proceedings of the ACM Symposium on Applied Computing. Vol. 1. 2005. p. 260-266
Huang, Heng ; Shen, Li ; Makedon, Fillia ; Zhang, Sheng ; Greenberg, Mark ; Gao, Ling ; Pearlman, Justin. / A Clustering-based approach for prediction of resynchronization therapy. Proceedings of the ACM Symposium on Applied Computing. Vol. 1 2005. pp. 260-266
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