DATA MINING FOR DETECTING DISTURBANCES IN HEART RHYTHM

摘要：

In this paper a framework for an objective and Interactive grading system of disturbances in heart rhythm is presented. The objects included in Hie study are transgenic mice which suffer from cardiac arrhythmias and their wild-type siblings. For all these mice long-time ECG recordings are available. The RR interval length are utilised to deduce statistical features on short-time heartbeat patterns. We demonstrate that these features are biologically relevant to classify the heartbeat patterns by a K-means clustering approach. Additionally, an extension of K-means is proposed, which consider user-defined constraints. The results of constraint-based clustering methods enable significant mid- and long-time analysis studies.

关键词： Data mining clustering cardiac arrhythmias, atrial fibrillation constrained K-means

作者: KAI ROTHAUS XIAOYI JIANG THOMAS WALDEYER LARISSA FARITZ MATHIS VOGEL PAULUS KIRCHHOF

作者单位: Department of Computer Science, University of M(u)nster, Einsteinstra?e 62, 48149 M(u)nster, Germany Department of Cardiology and Angiology, University of M(u)nster, Albert-Schweifzer-Str 33,48149 M(u)

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3211-3216

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）