Open AccessEfficient compression technique based on temporal modelling of ECG signal using principle component analysis
Author(s) -
Kumar Ranjeet,
Kumar Anil,
Singh G.K.,
Lee HeungNo
Publication year - 2017
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0360
Subject(s) - compression ratio , beat (acoustics) , computer science , data compression , correlation , signal compression , signal (programming language) , compression (physics) , pattern recognition (psychology) , correlation coefficient , signal processing , speech recognition , root mean square , artificial intelligence , acoustics , mathematics , engineering , telecommunications , materials science , physics , electrical engineering , radar , programming language , geometry , machine learning , automotive engineering , composite material , internal combustion engine
This study presents an improved technique for compression of electrocardiogram (ECG) signals, based on beat correlation of signal and principle component (PC) analysis, for ECG signal. For this purpose, two‐dimensional matrix of ECG signal based on temporal inter‐and intra‐beat correlation is constructed, and further compression is achieved using PC extraction. Beat correlation helps to generate very few PCs that increase the compression efficiency. A detailed analysis has been presented for ten signals having different rhythms, wave morphologies and abnormalities of Massachusetts Institute of Technology ‐ Beth Israel Hospital (MIT‐BIH) arrhythmia database. The effectiveness of the proposed method is examined with several attributes such as percentage root‐mean‐square difference, compression ratio, signal‐to‐noise ratio and correlation. Experimental results have shown that this method is very efficient for compression and suitable for different applications of telecardiology.