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 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.
Department of Computer Science, Chonnam National University, Gwangju 500-757, Republic of Korea.
Ngoc Anh Nguyen
NA
Thi
Department of Computer Science, Chonnam National University, Gwangju 500-757, Republic of Korea.
Hyung-Jeong
HJ
Yang
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
SunHee
S
Kim
Department of Computer Science, Chonnam National University, Gwangju 500-757, Republic of Korea.
Luu Ngoc
LN
Do
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000503:physiopathology; D016000:Cluster Analysis; D004562:Electrocardiography / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D016014:Linear Models; D025341:Principal Component Analysis; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors