Fiducial Features Extraction for ECG Signals using State-Space Unbiased FIR Smoothing

Investigation of electrocardiogram (ECG) signals is of importance to the medical community because heart diseases are among the principal causes of death in the world. Measurement noise typically does not allow for an accurate extraction of the ECG signal features. Therefore, much attention has been paid in recent decades to design efficient algorithms. In this paper, we propose using the unbiased finite impulse response (UFIR) smoothing technique to extract signal features in state space. The UFIR-based algorithm designed is compared to methods based on the wavelet transform, morphological transform, and threshold-based detector. Higher accuracy of the estimator proposed is demonstrated in applications to signal denoising and features extraction employing real ECG data.

[1]  K. Chan,et al.  Characteristic wave detection in ECG signal using morphological transform , 2005, BMC cardiovascular disorders.

[2]  Yuriy S. Shmaliy,et al.  Unbiased FIR denoising of ECG signals , 2017, 2017 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[3]  Yuriy S. Shmaliy,et al.  An Iterative Kalman-Like Algorithm Ignoring Noise and Initial Conditions , 2011, IEEE Transactions on Signal Processing.

[4]  Adrian D. C. Chan,et al.  Wavelet Distance Measure for Person Identification Using Electrocardiograms , 2008, IEEE Transactions on Instrumentation and Measurement.

[5]  Sabir Jacquir,et al.  Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT , 2016, Biomed. Signal Process. Control..

[6]  José M. P. Nascimento,et al.  Conference on Electronics , Telecommunications and Computers – CETC 2013 Embedded system for individual recognition based on ECG Biometrics , 2014 .

[7]  Jocelyne Fayn,et al.  Can the lessons learned from the assessment of automated electrocardiogram analysis in the Common Standards for quantitative Electrocardiography study benefit measurement of delayed contrast-enhanced magnetic resonance images? , 2007, Journal of electrocardiology.

[8]  Syed Abdul Rahman Al-Haddad,et al.  ECG biometric authentication based on non-fiducial approach using kernel methods , 2016, Digit. Signal Process..

[9]  Abdulmotaleb El-Saddik,et al.  ECG Authentication for Mobile Devices , 2016, IEEE Transactions on Instrumentation and Measurement.

[10]  U. Rajendra Acharya,et al.  Current methods in electrocardiogram characterization , 2014, Comput. Biol. Medicine.

[11]  L. Biel,et al.  ECG analysis: a new approach in human identification , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[12]  I. K. Daskalov,et al.  Automatic detection of the electrocardiogram T-wave end , 1999, Medical & Biological Engineering & Computing.

[13]  G. W. Hoffler,et al.  A spectral analysis of the normal resting electrocardiogram. , 1973, IEEE transactions on bio-medical engineering.

[14]  C. Ahn,et al.  Unbiased Finite Impluse Response Filtering: An Iterative Alternative to Kalman Filtering Ignoring Noise and Initial Conditions , 2017, IEEE Control Systems.

[15]  Ana L. N. Fred,et al.  ECG signals for biometric applications - are we there yet? , 2014, 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO).

[16]  M Bahoura,et al.  DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis. , 1997, Computer methods and programs in biomedicine.

[17]  Yuriy S. Shmaliy,et al.  A thinning algorithm for GPS-based unbiased FIR estimation of a clock TIE model , 2008 .

[18]  Yuriy S. Shmaliy,et al.  Optimal Memory for Discrete-Time FIR Filters in State-Space , 2014, IEEE Trans. Signal Process..

[19]  Abdelmalik Taleb-Ahmed,et al.  R-peaks detection based on stationary wavelet transform , 2015, Comput. Methods Programs Biomed..

[20]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[21]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[22]  Wei Lu,et al.  Feature fusion for imbalanced ECG data analysis , 2018, Biomed. Signal Process. Control..

[23]  Yuriy S. Shmaliy,et al.  An unbiased p-step predictive FIR filter for a class of noise-free discrete-time models with independently observed states , 2009, Signal Image Video Process..

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

[25]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[26]  I. S. Siva Rao.  Performance Identification of Different Heart Diseases Based On Neural Network Classification , 2016 .

[27]  Saeid Nahavandi,et al.  Human Identification From ECG Signals Via Sparse Representation of Local Segments , 2013, IEEE Signal Processing Letters.

[28]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[29]  Yuriy S. Shmaliy,et al.  Optimal and unbiased FIR filtering in discrete time state space with smoothing and predictive properties , 2012, EURASIP Journal on Advances in Signal Processing.

[30]  Carlo Sansone,et al.  Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction , 2015, Comput. Methods Programs Biomed..

[31]  Yuriy S. Shmaliy,et al.  FIR Smoothing of Discrete-Time Polynomial Signals in State Space , 2010, IEEE Transactions on Signal Processing.

[32]  Ivo Provaznik,et al.  Adaptive Wavelet Wiener Filtering of ECG Signals , 2013, IEEE Transactions on Biomedical Engineering.

[33]  Paolo Bifulco,et al.  Influence of QT correction on temporal and amplitude features for human identification via ECG , 2013, 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications.

[34]  Kang-Ping Lin,et al.  QRS feature extraction using linear prediction , 1989, IEEE Transactions on Biomedical Engineering.

[35]  Y.S. Shmaliy,et al.  An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.