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: Contribution to conferencePaperpeer-review

3 Scopus citations


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)
Number of pages7
StatePublished - Dec 1 2005
Externally publishedYes
Event20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States
Duration: Mar 13 2005Mar 17 2005


Other20th Annual ACM Symposium on Applied Computing
CountryUnited States
CitySanta Fe, NM


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

ASJC Scopus subject areas

  • Software

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