NDLI: Data mining for detecting disturbances in heart rhythm

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Proceedings. International Conference on Machine Learning and Cybernetics

Data mining for detecting disturbances in heart rhythm

Content Provider	IEEE Xplore Digital Library
Author	Rothaus, K. Xiaoyi Jiang Waldeyer, T. Faritz, L. Vogel, M. Kirchhof, P.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Munster, Munster (Rothaus, K.; Xiaoyi Jiang)
Abstract	In this paper a framework for an objective and interactive grading system of disturbances in heart rhythm is presented. The objects included in the 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.
Starting Page	3211
Ending Page	3216
File Size	3464211
Page Count	6
File Format	PDF
ISBN	9781424420957
DOI	10.1109/ICMLC.2008.4620960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Cybernetics Conferences constrained K-means Data mining clustering cardiac arrhythmias atrial fibrillation
Content Type	Text
Resource Type	Article

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