Information theoretic multiscale truncated SVD for multilead electrocardiogram. Issue 129 (June 2016)

• Record Type: Journal Article
• Authors: Sharma, L.N.
• Is Part Of: Computer methods and programs in biomedicine. Issue 129(2016)
• Page Start: 109
• Page End: 116
• Publication Date: 2016-06
• Subjects: Multilead ECG -- Multiscale SVD -- Multivariate multiscale entropy -- PRD -- RMSE; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.01.010
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Highlights: Clinically relevant multiscale multivariate entropy is introduced based on modified Shannon's entropy. Multiscale truncated SVD is formulated by reducing ranks of the matrices based on multiscale multivariate entropy and energy. The proposed algorithm is tested with normal and pathological signals. Signal distortion measures like PRD, RMSE, WEDD are evaluated. A new signal distortion measure, multivariate clinical distortion (MCD), is introduced. Mean opinion scores (MOS) are evaluated to ensure the quality of reconstructed signals. Abstract: Background and objective: In this paper an information theory based multiscale singular value decomposition (SVD) is proposed for multilead electrocardiogram (ECG) signal processing. The shrinkage of singular values for different multivariate multiscale matrices at wavelet scales is based on information content. It aims to capture and preserve the information of clinically important local waves like P-waves, Q-waves, T-waves and QRS-complexes. Methods: The information is derived through clinically relevant multivariate multiscale entropy in SVD domain modifying Shannon's entropy. This optimizes the approximate ranks for matrices to capture the clinical components of ECG signals appearing at different scales. A newly introduced multivariate clinical distortion (MCD) metric is computed and compared with existing subjective and objective signal distortion measures. The proposed method is tested with records from CSE multilead measurement library and PTB diagnostic ECG database for various pathological cases. Results: It gives average percentage root mean square difference (PRD), average normalized root mean square error (NRMSE), average wavelet energy based diagnostic distortion measure (WEDD) values 5.8879%, 0.0059 and 1.0760% respectively for myocarditis pathology. The corresponding MCD value is 1.9429%. The highest average PRD and average WEDD values are 11.4053% and 5.5194% for cardiomyopathy with the corresponding MCD value 1.4003%. Conclusions: Based on WEDD values and mean opinion scores (MOS), the quality group of all processed signals fall under excellent category.