A Harmonic Linear Dynamical System for Prominent ECG Feature Extraction. (26th February 2014)

• Record Type: Journal Article
• Authors: Nguyen Thi, Ngoc Anh; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc
• Other Names: Cikajlo Imre Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2014(2014)
• Publication Date: 2014-02-26
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2014/761536
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.