Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis

This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches. All Rights Reserved © 2015 Universidad Nacional Autonoma de Mexico, Centro de Ciencias Aplicadas y Desarrollo Tecnologico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.

[1]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

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

[3]  D. White,et al.  Heart rate chaos in obstructive sleep apnea in children , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Emmanuel Skordalakis,et al.  Syntactic Pattern Recognition of the ECG , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Mohammad B. Shamsollahi,et al.  ECG denoising using modulus maxima of wavelet transform , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Liang-Yu Shyu,et al.  Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.

[7]  S. Sideris,et al.  Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. , 1998, American heart journal.

[8]  E. Farahabadi,et al.  R Peak Detection in Electrocardiogram Signal Based on an Optimal Combination of Wavelet Transform, Hilbert Transform, and Adaptive Thresholding , 2011, Journal of medical signals and sensors.

[9]  Friso De Boer,et al.  Improved QRS Detection Algorithm using Dynamic Thresholds , 2009 .

[10]  Kap Luk Chan,et al.  Classification of electrocardiogram using hidden Markov models , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[11]  Herbert F. Voigt,et al.  IEEE Engineering in Medicine and Biology Society , 2019, IEEE Transactions on Biomedical Engineering.

[12]  P. C. Cortez,et al.  A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique. , 2007, Medical engineering & physics.

[13]  Xing Hongyan,et al.  A New QRS Detection Algorithm Based on Empirical Mode Decomposition , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[14]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[15]  Pablo Laguna,et al.  Multilead Analysis of T-Wave Alternans in the ECG Using Principal Component Analysis , 2009, IEEE Transactions on Biomedical Engineering.

[16]  S. AsirvadamVijanth,et al.  ECG viewed using grayscale patterns , 2009 .

[17]  Derek Abbott,et al.  Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems , 2014, PloS one.

[18]  Fei Zhang,et al.  Novel QRS Detection by CWT for ECG Sensor , 2007, 2007 IEEE Biomedical Circuits and Systems Conference.

[19]  G.G. Cano,et al.  QRS detection based on hidden Markov modeling , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[20]  D. Kumar,et al.  of the 23 rd Annual EMBS International Conference , October 25-28 , Istanbul , Turkey Wavelets for QRS Detection , 2022 .

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

[22]  Szi-Wen Chen,et al.  A moving average based filtering system with its application to real-time QRS detection , 2003, Computers in Cardiology, 2003.

[23]  I. Romero.  PCA-based noise reduction in ambulatory ECGs , 2010, 2010 Computing in Cardiology.

[24]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[25]  黄亚明.  PhysioBank , 2009 .

[26]  Xiao-Li Yang,et al.  The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition , 2008, 2008 Fourth International Conference on Natural Computation.

[27]  R. Orglmeister,et al.  QRS Detection Using Zero Crossing Counts , 2003 .

[28]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[29]  Willis J. Tompkins,et al.  Automated High-Speed Analysis of Holter Tapes with Microcomputers , 1983, IEEE Transactions on Biomedical Engineering.

[30]  A. Alesanco,et al.  A novel real-time multilead ECG compression and de-noising method based on the wavelet transform , 2003, Computers in Cardiology, 2003.

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

[32]  Samarendra Dandapat,et al.  Clinical Entropy based PCA for Multi-lead Electrocardiogram Signals , 2012, IAIT 2012.

[33]  A. Alwan.  Global status report on noncommunicable diseases 2010. , 2011 .

[34]  Truong Q. Nguyen,et al.  Filter bank-based processing of the stress ECG , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[35]  J. Trinder,et al.  Autonomic activity during human sleep as a function of time and sleep stage , 2001, Journal of sleep research.

[36]  Oussama Wazni,et al.  Characterization of Premature Atrial Contraction Activity Prior to the Onset of Postoperative Atrial Fibrillation in Cardiac Surgery Patients , 2004 .

[37]  Dimitrios I. Fotiadis,et al.  Arrhythmia classification using the RR-interval duration signal , 2002, Computers in Cardiology.

[38]  Patrick Gaydecki,et al.  The use of the Hilbert transform in ECG signal analysis , 2001, Comput. Biol. Medicine.

[39]  Fethi Bereksi-Reguig,et al.  QRS complex detection based on multi wavelet packet decomposition , 2011, Appl. Math. Comput..

[40]  P. Morizet-Mahoudeaux,et al.  Simple microprocessor-based system for on-line e.c.g. arrhythmia analysis , 2006, Medical and Biological Engineering and Computing.

[41]  R. Sukanesh,et al.  Experimental Studies on Intelligent, Wearable and Automated Wireless Mobile Tele-Alert System for Continuous Cardiac Surveillance , 2013 .

[42]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

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

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

[45]  James McNames,et al.  Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes , 2004, IEEE Transactions on Biomedical Engineering.

[46]  Masahiko Okada,et al.  A Digital Filter for the ORS Complex Detection , 1979, IEEE Transactions on Biomedical Engineering.

[47]  Philip Langley,et al.  Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration , 2010, IEEE Transactions on Biomedical Engineering.

[48]  Marian Kotas,et al.  Projective filtering of time-aligned ECG beats for repolarization duration measurement , 2007, Comput. Methods Programs Biomed..

[49]  Ying Liu,et al.  Adaptive Threshold for QRS Complex Detection Based on Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[50]  Huilong Duan,et al.  A QRS Complex Detection Algorithm Based on Mathematical Morphology and Envelope , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[51]  Mamun,et al.  Hardware approach of R-peak detection for the measurement of fetal and maternal heart rates , 2012 .