Reconstruction for ECG Compressed Sensing Using a Time-Normalized PCA Dictionary

Reconstruction for ECG Compressed Sensing Using a Time-Normalized PCA Dictionary

Compressed sensing (CS), due to its computational simplicity is a perspective data reduction technique for remote ECG monitoring applications. In this paper, a novel method of reconstruction for CS of ECG signal is proposed, which uses a time-normalized agnostic dictionary created by the principal c...

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Bibliographic Details
Published in:2019 12th International Conference on Measurement pp. 30 - 33
Main Authors: Dolinsky, Pavol, Andras, Imrich, Saliga, Jan, Michaeli, Linus
Format: Conference Proceeding
Language:English
Published: Institute of Measurement Science, Slovak Academy of Sciences 01-05-2019
Subjects:
Compressed sensing
Dictionaries
ECG
Electrocardiography
Matching pursuit algorithms
Nickel
PCA
Principal component analysis
QRS Detection
Training
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Summary:Compressed sensing (CS), due to its computational simplicity is a perspective data reduction technique for remote ECG monitoring applications. In this paper, a novel method of reconstruction for CS of ECG signal is proposed, which uses a time-normalized agnostic dictionary created by the principal component analysis (PCA) of training signals. The proposed method exploits a QRS detector to split the input signal into variable-size frames and shows significantly better reconstruction quality compared against traditional orthogonal matching pursuit (OMP) approach with Mexican hat and Symlet4 wavelet dictionaries.
DOI:10.23919/MEASUREMENT47340.2019.8779960