Mohammed Assam OUALI, Mouna GHANAI, Kheireddine CHAFAA

Upper envelope detection of ECG signals for baseline wander correction: a pilot study

Baseline wander (BW) is a common low frequency artifact in electrocardiogram (ECG) signals. The primecause from which BW arises is the patient’s breathing and movement. To facilitate reliable visual interpretation ofthe ECG and to discern particular patterns in the ECG signal, BW needs to be removed. In this paper, a novel BWremoval method is presented. The hypothesis is based on the observation that ECG signal variation covaries with itsBW. As such, the P, Q, R, S, and T peaks will follow the baseline drift. On this basis, the following proposition istrue: a reliable approximation of the baseline drift can be obtained from the shape derived from the interpolation of oneform of the ECG signal peak (peak envelope). The simulation was performed by adding artificial BW to ECG signalrecordings. The signal-to-noise ratio, mean squared error, and improvement factor criteria were used to numericallyevaluate the performance of the proposed approach. The technique was compared to that of the Hilbert vibrationdecomposition method, an empirical-mode decomposition technique and mathematical morphology. The results of thesimulation indicate that the proposed technique is most effective in situations where there is a considerable distortion inthe baseline wandering.

PDF

___

[1] Fischbach M. Guide Pratique du Cardiaque. Paris, France: Editions Frison-Roche, 2002 (in French).
[2] Venes D. Taber’s Cyclopedic Medical Dictionary. New York, NY, USA: FA Davis, 2017.
[3] Saladin KS. Anatomy and Physiology: The Unity of Form and Function. New York, NY, USA: WCB/McGraw-Hill, 1998.
[4] Jenkal W, Latif R, Toumanari A, Dliou A, El B’charri O, Maoulainine FMR. QRS detection based on an advanced multilevel algorithm. Int J Adv Comp App 2016; 1: 253-60.
[5] Hung K, Zhang YT. Implementation of a WAP-based telemedicine system for patient monitoring. IEEE T Inf Technol B 2003; 7: 101-107.
[6] Salvador CH, Carrasco MP, Gonz´alez de Mingo MA, Mu˜noz Carrero A, M´arquez Montes J, Sosa Mart´ın L, Cavero MA, Fern´andez Lozano I, Monteagudo JL. A GSM and internet services-based system for out of hospital follow-up of cardiac patients. IEEE T Inf Technol B 2005; 9: 73-84.
[7] Rodriguez J, Goni A, Illarramendi A. Real-time classification of ECGs on a PDA. IEEE T Inf TechnoL B 2005; 9: 23-34.
[8] Ji TY, Wu QH. Baseline normalisation of ECG signals using empirical mode decomposition and mathematical morphology. Electron Lett 2008; 44: 82-83.
[9] van Alste JA, van Eck W, Herrmann OE. ECG baseline wander reduction using linear phase filters. Comput Biomed Res 1986; 19: 417-427.
[10] Ciarlini P, Barone P. A recursive algorithm to compute the baseline drift in recorded biological signals. Comput Biomed Res 1988; 21: 221-226.
[11] Meyer CR, Keiser HN. Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput Biomed Res 1977; 10: 459-470.
[12] Seygullah HO, Muka HA. A morphology based algorithm for baseline wander elimination in ECG records. In: IEEE 1992 Proceedings of the International Conference Biomedical Engineering Days; 1992; ˙Istanbul, Turkey. pp. 157-160.
[13] Laguna P, Jane R, Caminal P. Adaptive filtering of ECG baseline wander. In: 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 1992. pp. 508-509.
[14] Nitish VT, Zhu YS. Application of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE T Bio-Med Eng 1991; 38: 785-794.
[15] Chen H, Huang K, Jiang Y. Detection of ST segment in electrocardiogram by wavelet transform. Electr Mach Control 2006; 10: 531-533.
[16] Shi L, Yang C, Fei M. Electrocardiogram R-wave and ST segment extraction based on wavelet transform. Chin J Sci Instrum 2008; 29: 804-809.
[17] Sharma H, Sharma KK. Baseline wander removal of ECG signals using Hilbert vibration decomposition. Electron Lett 2015; 51: 447-449.
[18] Fasano A, Villani V. Baseline wander removal for bioelectrical signals by quadratic variation reduction. Signal Process 2014; 99: 48-57.
[19] Sakshi A, Anubha G. Fractal and EMD based removal of baseline wander and powerline interference from ECG signals. Comput Biol Med 2013; 43: 1889-1899.
[20] Zivanovic M, Gonz´alez-Izal M. Simultaneous powerline interference and baseline wander removal from ECG and EMG signals by sinusoidal modeling. Med Eng Phys 2013; 35: 1431-1441.
[21] Leski JM, Henzel N. ECG baseline wander and powerline interference reduction using nonlinear filter bank. Signal Process 2005; 85: 781-793.
[22] Rameshwari M, Cheeran AN, Vaibhav DA. Improved technique to remove ECG baseline wander–application to Pan & Tompkins QRS detection algorithm. Comm Com Inf Sc 2013: 361: 492-499.
[23] Zou C, Qin Y, Sun C, Li W, Chen W. Motion artifact removal based on periodical property for ECG monitoring with wearable systems. Pervasive Mob Comput 2017; 40: 267-278.
[24] Xiao H, Zhong X, Ni Z. Removal of baseline wander from ECG signal based on a statistical weighted moving average filter. J Zhejiang U-Sci C 2011; 12: 397-403.
[25] Blanco-Velasco M, Weng B, Barner KE. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput Biol Med 2008; 38: 1-13.
[26] Gupta P, Sharma KK, Joshi SD. Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition. Healthcare Technology Letters 2015; 2: 164-166.
[27] Niederhauser T, Wyss-Balmer T, Haeberlin A, Marisa T, Wildhaber RA, Goette J, Jacomet M, Vogel R. Graphicsprocessor-unit-based parallelization of optimized baseline wander filtering algorithms for long-term electrocardiography. IEEE T Biomed Eng 2015; 62: 576-1584.
[28] Niederhauser T, Marisa T, Kohler L, Haeberlin A, Wildhaber RA, Ab¨acherli R, Goette J, Jacomet M, Vogel R. A baseline wander tracking system for artifact rejection in long-term electrocardiography. IEEE T Biomed Circ S 2016; 10: 255-265.
[29] Alarka S, Arijit B, Abhijit L. Application of framelet transform in filtering baseline drift from ECG signals. Procedia Technology 2012; 4: 862-866.
[30] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM. {PhysionBank, PhysioToolkit, and PhysioNet }: Components of a new research resource for complex physiologic signals. Circulation 2000; 101: e215-e220.
[31] McSharry PE, Clifford GD, Tarassenko L, Smith L. A dynamical model for generating synthetic electrocardiogram signals. IEEE T Bio-med Eng 2003; 50: 289-294.
[32] Yi L, Liu Z, Wang K, Chen M, Peng S, Zhao W, He J, Zhao G. A new background subtraction method for energy dispersive X-ray fluorescence spectra using a cubic spline interpolation. Nucl Instrum Meth A 2015; 775: 12-14.
[33] Warlar R, Eswaran C. Integer coefficient bandpass filter for the simultaneous removal of baseline wander, 50 and 100 Hz interference from the ECG. Biol Eng Comput 1991; 29: 333-336.
[34] S¨ornmo L, Laguna P. ECG Signal Processing. Bioelectrical Signal Processing in Cardiac and Neurological Applications. Amsterdam, the Netherlands: Elsevier Academic Press, 2005.
[35] Azuaje F, Clifford GD, McSharry PE. Advanced Methods and Tools for ECG Data Analysis. Boston, MA, USA: Artech House, Inc., 2006.
[36] Sheffield LT, Berson A, Bragg-Remschel D, Gillette PC, Hermes RE, Hinkle L, Kennedy H, Mirvis DM, Oliver C. Recommendations for standards of instrumentation and practice in the use of ambulatory electrocardiography: AHA Special Report. Circulation 1985; 171: 626A-636A.
[37] Kligfield P, Gettes LS, Bailey JJ, Childers R, Deal BJ, Hancock EW, van Herpen G, Kors JA, Macfarlane P, Mirvis DM et al. Recommendations for the standardization and interpretation of the electrocardiogram: Part I: the electrocardiogram and its technology. Circulation 2007; 115: 1306-1324.