ECG feature extraction using principal component analysis for studying the effect of diabetes

Abstract The condition of cardiac health is given by Electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of Biomedical and healthcare. The precision in the identification of various parameters in ECG is of great importance. Many algorithms have been developed in the last few years for this purpose. Since diabetes is the major chronic illness prevailing today, recently there has been increasing interest in the study of the relationship between diabetes and cardiac health. This paper presents an algorithm based on Principal Component Analysis (PCA) for 12 lead ECG feature extraction and the estimation of diabetes-related ECG parameters. The data used for our purpose is acquired by XBio Aqulyser unit from TMI systems. The baseline wander is removed from the acquired data using the FFT approach and the signal is de-noised using wavelet transform and then the PCA method is employed to extract the R-wave. The other waves are then extracted using the window method. Later, using these primary features, the diabetes mellitus (DM)-related features like corrected QT interval (QTc), QT dispersion (QTd), P wave dispersion (PD) and ST depression (STd) are estimated. This study has taken 25 diabetic patients data for study.

[1]  Pedro Virgilio Rivera Farina,et al.  QTc analysis and comparison in pre-diabetic patients , 2010, 2010 Computing in Cardiology.

[2]  M. del Carmen Raola,et al.  Real-time QT interval measurement , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[3]  S. T. Hamde,et al.  Estimation of ECG features using LabVIEW , 2009 .

[4]  Pablo Laguna,et al.  Principal Component Analysis in ECG Signal Processing , 2007, EURASIP J. Adv. Signal Process..

[5]  L. Ludeman,et al.  Discrimination of cardiac arrhythmias using a fuzzy rule-based method , 1994, Computers in Cardiology 1994.

[6]  Richard B Devereux,et al.  Electrocardiographic repolarization complexity and abnormality predict all-cause and cardiovascular mortality in diabetes: the strong heart study. , 2004, Diabetes.

[7]  A. Schuck,et al.  QRS detector pre-processing using the complex wavelet transform , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[8]  S. Stern,et al.  The ECG in Diabetes Mellitus , 2009, Circulation.

[9]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[10]  Yuanyuan Wang,et al.  QRS Complexes Detection by Using the Principal Component Analysis and the Combined Wavelet Entropy for 12-Lead Electrocardiogram Signals , 2009, 2009 Ninth IEEE International Conference on Computer and Information Technology.

[11]  Christodoulos Stefanadis,et al.  The ECG Vertigo in Diabetes and Cardiac Autonomic Neuropathy , 2011, Experimental diabetes research.

[12]  A. Geva,et al.  ECG feature extraction using optimal mother wavelet , 2000, 21st IEEE Convention of the Electrical and Electronic Engineers in Israel. Proceedings (Cat. No.00EX377).

[13]  W. Zong,et al.  Automated ECG rhythm analysis using fuzzy reasoning , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[14]  H. K. Verma,et al.  ECG Modeling and QRS Detection using Principal Component Analysis , 2006 .

[15]  B. Celler,et al.  Using wavelet coefficients for the classification of the electrocardiogram , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[16]  Peter W. Macfarlane,et al.  Review: Diabetes and the QT interval: time for debate , 2004 .

[17]  V. C. Veera Reddy,et al.  CARDIAC ARRHYTHMIA CLASSIFICATION USING FUZZY CLASSIFIERS , 2008 .

[18]  Mahantapas Kundu,et al.  A knowledge-based approach to ECG interpretation using fuzzy logic , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[19]  C. Purdy,et al.  Data-specific signal denoising using wavelets, with applications to ECG data , 2004, The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04..

[20]  A. Ahmadian,et al.  ECG Feature Extraction Based on Multiresolution Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[21]  Cesar Ramos Rodrigues,et al.  ADAPTIVE ECG FILTERING AND QRS DETECTION USING ORTHOGONAL WAVELET TRANSFORM , .

[22]  Hongjun Zhu,et al.  The ECG recording and analysis instrumentation based on virtual instrument technology and continuous wavelet transform , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[23]  M.P.S. Chawla,et al.  Parameterization and R-Peak Error Estimations of ECG Signals Using Independent Component Analysis , 2007 .

[24]  Felipe E. Olvera.  Electrocardiogram Waveform Feature Extraction Using the Matched Filter , 2001 .

[25]  M. Jacobson,et al.  Non-invasive clinical screening of diabetes and coronary heart disease , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..

[26]  Hlaing Minn,et al.  A Patient-Adaptive Profiling Scheme for ECG Beat Classification , 2010, IEEE Transactions on Information Technology in Biomedicine.

[27]  D. T. Kaplan.  Simultaneous QRS detection and feature extraction using simple matched filter basis functions , 1990, [1990] Proceedings Computers in Cardiology.

[28]  Ahmet Soylu,et al.  The effect of diabetes mellitus on the P-wave dispersion. , 2007, Circulation journal : official journal of the Japanese Circulation Society.

[29]  G. Boudreaux-Bartels,et al.  Wavelet transform-based QRS complex detector , 1999, IEEE Transactions on Biomedical Engineering.

[30]  Dan Lascu,et al.  LabVIEW electrocardiogram event and beat detection , 2008 .

[31]  Fayyaz A. Afsar,et al.  A Comparison of Baseline Removal Algorithms for Electrocardiogram (ECG) Based Automated Diagnosis of Coronory Heart Disease , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[32]  S. T. Hamde,et al.  Feature extraction from ECG signals using wavelet transforms for disease diagnostics , 2002, Int. J. Syst. Sci..