A Harmonic Linear Dynamical System for Prominent ECG Feature Extraction

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...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2014-01, Vol.2014 (2014), p.1-8
Main Authors: Nguyen Thi, Ngoc Anh, Yang, Hyung-Jeong, Kim, Sun-Hee, Do, Luu Ngoc
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmias, Cardiac - physiopathology
Cluster Analysis
Electrocardiography - methods
Fourier Analysis
Humans
Linear Models
Principal Component Analysis
Regression Analysis
Signal Processing, Computer-Assisted
Time Factors
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Summary: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.
ISSN:1748-670X
1748-6718
DOI:10.1155/2014/761536