Multilead ECG data compression using SVD in multiresolution domain

Abstract In this paper, multilead electrocardiogram (MECG) data compression using singular value decomposition in multiresolution domain is proposed. It ensures a high compression ratio by exploiting both intra-beat and inter-lead correlations. A new thresholding technique based on multiscale root fractional energy contribution is proposed. It selects the singular values depending on the clinical importance of the wavelet subbands. The proposed method is evaluated with the PTB Diagnostic ECG database. This compression method is embedded with a pulse amplitude modulated direct sequence-ultra wideband technology for transmission of the MECG data. This may be useful in telemonitoring services for the wireless body sensor network. A comparative study of computational time complexity has also been carried out. The results show that the proposed method can be executed at least three times faster than the existing methods. The storage efficiency is enhanced by 19 times using this method.

[1]  Amjed S. Al-Fahoum,et al.  Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  S. Dandapat,et al.  ECG signal denoising using higher order statistics in Wavelet subbands , 2010, Biomed. Signal Process. Control..

[3]  Yu Hen Hu,et al.  Power-Line Interference Detection and Suppression in ECG Signal Processing , 2008, IEEE Transactions on Biomedical Engineering.

[4]  M. R. Yuce,et al.  Transmit-Only Ultra Wide Band Body Sensors and Collision Analysis , 2013, IEEE Sensors Journal.

[5]  S M Ahmed,et al.  A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform , 2007, Journal of medical engineering & technology.

[6]  Abraham Otero,et al.  A new algorithm for wavelet-based heart rate variability analysis , 2013, Biomed. Signal Process. Control..

[7]  I. S. N. Murthy,et al.  ECG Data Compression Using Fourier Descriptors , 1986, IEEE Transactions on Biomedical Engineering.

[8]  Mehmet Rasit Yuce,et al.  Implementation of wireless body area networks for healthcare systems , 2010 .

[9]  M. Alex O. Vasilescu,et al.  Multilinear (Tensor) Image Synthesis, Analysis, and Recognition [Exploratory DSP] , 2007, IEEE Signal Processing Magazine.

[10]  Nai-Kuan Chou,et al.  ECG data compression using truncated singular value decomposition , 2001, IEEE Trans. Inf. Technol. Biomed..

[11]  Tzyy-Ping Jung,et al.  Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning , 2012, IEEE Transactions on Biomedical Engineering.

[12]  Bashar A. Rajoub An efficient coding algorithm for the compression of ECG signals using the wavelet transform , 2002, IEEE Transactions on Biomedical Engineering.

[13]  Borys Surawicz,et al.  Chou's Electrocardiography in Clinical Practice , 2020 .

[14]  M. Sabarimalai Manikandan,et al.  Wavelet energy based diagnostic distortion measure for ECG , 2007, Biomed. Signal Process. Control..

[15]  Pierre Vandergheynst,et al.  Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes , 2011, IEEE Transactions on Biomedical Engineering.

[16]  David S. Watkins,et al.  Fundamentals of matrix computations , 1991 .

[17]  A. Cohen,et al.  ECG compression using long-term prediction , 1993, IEEE Transactions on Biomedical Engineering.

[18]  H. Koymen,et al.  Multichannel ECG data compression by multirate signal processing and transform domain coding techniques , 1993, IEEE Transactions on Biomedical Engineering.

[19]  Guerino Giancola,et al.  Understanding Ultra Wide Band Radio Fundamentals , 2004 .

[20]  Daibashish Gangopadhyay,et al.  Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[21]  Gene H. Golub,et al.  Matrix computations , 1983 .

[22]  M. Sabarimalai Manikandan,et al.  Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM , 2006, Biomed. Signal Process. Control..

[23]  Samarendra Dandapat,et al.  Multichannel ECG Data Compression Based on Multiscale Principal Component Analysis , 2012, IEEE Transactions on Information Technology in Biomedicine.

[24]  Manuel Blanco-Velasco,et al.  Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems , 2014, IEEE Journal of Biomedical and Health Informatics.

[25]  T. Moon,et al.  Mathematical Methods and Algorithms for Signal Processing , 1999 .

[26]  Jack Dongarra,et al.  LINPACK Users' Guide , 1987 .

[27]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[28]  M. Sabarimalai Manikandan,et al.  Multiscale Entropy-Based Weighted Distortion Measure for ECG Coding , 2008, IEEE Signal Processing Letters.

[29]  Ryuji Kohno,et al.  Ultra Wideband Signals and Systems in Communication Engineering: Ghavami/Ultra Wideband Signals and Systems in Communication Engineering , 2004 .

[30]  Arnon D. Cohen,et al.  The weighted diagnostic distortion (WDD) measure for ECG signal compression , 2000, IEEE Transactions on Biomedical Engineering.

[31]  Willis J. Tompkins,et al.  A New Data-Reduction Algorithm for Real-Time ECG Analysis , 1982, IEEE Transactions on Biomedical Engineering.

[32]  Jianqing Li,et al.  A Dynamic Compression Scheme for Energy-Efficient Real-Time Wireless Electrocardiogram Biosensors , 2014, IEEE Transactions on Instrumentation and Measurement.