Correction of the recording artifacts and detection of the functional deviations in ECG by means of syndrome decoding with an automatic burst error correction of the cyclic codes using periodograms for determination of the code component spectral range. Part 1: basic principles of the novel approach

Автор: Adamoviс Evgenie D., Aleksandrov Pavel L., Gradov Oleg V., Mamalyga Leonid M., Mamalyga Maksim L.

Статья в выпуске: 6, 2015 года.

Бесплатный доступ

Aims. This paper describes a novel approach to the analysis of electrocardiographic data based on the consideration of the repetitive P, Q, R, S, T sequences as cyclic codes. In Part I we introduce a principle similar to the syndrome decoding using the control numbers, which allows correcting the noise combinations. Materials and methods. We propose to apply the burst-error-correcting algorithms for automatic detection of the ECG artifacts and the functional abnormalities, including those compared to the reference model. Our approach is compared to the symbolic dynamics methods. During the automated search of the code components (i.e. point values and spectral ranges one-to-one corresponding to P, Q, R, S, T) considered in Part II, the authors apply the Lomb-Scargle periodogram method with the phase control which allows to determine the code components not only from the main harmonics, but also using the sidebands, avoiding the phase errors. Results. The results of the method testing on rats with the heart failure using a simplified telemetric recording from the implantable chips are given in Part III...

Еще

Ecg, cyclic codes, error corrections, syndrome decoding, control numbers, lombscargle periodogram methods, fingerprinting

Короткий адрес: https://sciup.org/148308799

IDR: 148308799 | DOI: 10.12710/cardiometry.2015.6.6576

Список литературы Correction of the recording artifacts and detection of the functional deviations in ECG by means of syndrome decoding with an automatic burst error correction of the cyclic codes using periodograms for determination of the code component spectral range. Part 1: basic principles of the novel approach

Coumel P, Leclercq JF, Attuel P. Computerized Arrhythmia Analysis of the Holter Recordings. Develop. Cardiovasc. Med. 1984;37:82-92.
Bartolo A, Dzwonczyk RR, Roberts O, Goldman E. Description and validation of a technique for the removal of ECG contamination from diaphragmatic EMG signal. Med. & Biol. Eng. & Comput. 1996;34(1):76-81.
Vasyukevich V. Asynchronous Operators of Sequential Logic: Venjunction & Sequention. Berlin -Heidelberg: Springer; 2011. 126 p.
Moore GW, Hutchins GM, Bulkley BH. Certainty levels in the nullity method of symbolic logic: application to the pathogenesis of congenital heart malformations. Journ. Theor. Biol. 1979;76(1):53-81.
Moore GW, Hutchins GM. Symbolic logic analysis of congenital heart disease. Path. Res. Pract. 1981;171(1):59-85.
Brownlee RR, Shimmel-Golden JB, Del Marco CJ, Furman S. A new symbolic language for diagramming pacemaker/heart interaction. Pac. Clin. Electrophys. 1982;5(5):700-9.
Slavkovský P, Hulín I. Zobrazenie elektrického poľa serdca pomocou znakových máp. Bratis. Lek. Lis. 1985;83(5):530-42.
Rosenberg S, Itti R, Benjelloun L. Symbolic reasoning about myocardial scintigrams in PROLOG. Eur. Journ. Nucl. Med. 1986;12(2):65-8.
Tasca C, Stefaneanu L, Vasilescu C. The myocardial microangiopathy in human and experimental diabetes mellitus. (A microscopic, ultrastructural, morphometric and computer-assisted symboliclogic analysis). Endocrinologie, Apr-Jun 1986;24(2):59-69.
Wessel N, Schirdewan A, Malik M, Voss A. Symbolische Dynamik -eine eigenständige Methode zur Erkennung von nichtlinearen Phänomenen der Herzschlagregulation. Biomedizin. Tech. 1998;43:510-1.
Kurths J, Voss A, Saparin P, Witt A, Kleiner HJ, Wessel N. Quantitative analysis of heart rate variability. Chaos. 1995;5(1):88-94.
Cesarelli M, Romano M, Bifulco P, Improta G, D'Addio G. An application of symbolic dynamics for FHRV assessment. Stud. Health Technol. Inform. 2012;180:123-7.
Baumert M, Javorka M, Kabir MM. Joint symbolic analyses of heart rate, blood pressure, respiratory dynamics. Journ. Electrocard. 2013;46(6):569-73.
Yeragani VK, Nadella R, Hinze B, Yeragani S, Jampala VC. Nonlinear measures of heart period variability: decreased measures of symbolic dynamics in patients with panic disorder. Dep. Anx. 2000;12(2):67-77.
Kim JS, Park JE, Seo JD, Lee WR, Kim HS, Noh JI, Kim NS, Yum MK. Decreased entropy of symbolic heart rate dynamics during daily activity as a predictor of positive head-up tilt test in patients with alleged neurocardiogenic syncope. Phys. Med. Biol. 2000;45(11):3403-12.
Maestri R, Pinna GD, Accardo A, Allegrini P, Balocchi R, D'Addio G, Ferrario M, Menicucci D, Porta A, Sassi R, Signorini MG, La Rovere MT, Cerutti S. Nonlinear indices of heart rate variability in chronic heart failure patients: redundancy and comparative clinical value. Journ. Cardiovasc. Electrophysiol. 2007;18(4):425-33.
Magagnin V, Bassani T, Bari V, Turiel M, Maestri R, Pinna GD, Porta A. Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices. Physiol. Measur. 2011;32(11):1775-86.
Guzzetti S, Borroni E, Garbelli PE, Ceriani E, Della BP, Montano N, Cogliati C, Somers VK, Maliani A, Porta A. Symbolic dynamics of heart rate variability: a probe to investigate cardiac autonomic modulation. Circulation. 2005;112(4):465-70.
Tobaldini E, Porta A, Wei SG, Zhang ZH, Francis J, Casali KR, Weiss RM, Felder RB, Montano N. Symbolic analysis detects alterations of cardiac autonomic modulation in congestive heart failure rats. Aut. Neurosci. 2009;150(1-2):21-6.
Takahashi AC, Porta A, Melo RC, Quitério RJ, da Silva E, Borghi-Silva A, Tobaldini E, Montano N, Catai AM. Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis. Intern. Emerg. Med. 2012;7(3):229-35.
Cysarz D, Linhard M, Edelhäuser F, Längler A, Van Leeuwen P, Henze G, Seifert G. Symbolic patterns of heart rate dynamics reflect cardiac autonomic changes during childhood and adolescence. Aut. Neurosci. 2013;178(1-2):37-43.
Van Leeuwen P, Cysarz D, Lange S, Geue D, Groenemeyer D. Quantification of fetal heart rate regularity using symbolic dynamics. Chaos. 2007;17(1):1-9.
Van Leeuwen P, Cysarz D, Lange S, Grönemeyer D. Increase in regularity of fetal heart rate variability with age. Biomedizin. Tech. 2006;51(4):244-7.
Valencia JF, Vallverdú M, Cygankiewicz I, Voss A, Vazquez R, Luna AB, Caminal P. Multiscale regularity analysis of the Heart Rate Variability: stratification of cardiac death risk. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007; pp. 5947-50.
Cysarz D, Edelhäuser F, Van Leeuwen P. Multiscale analysis of acceleration and deceleration of the instantaneous heart rate using symbolic dynamics. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011; pp. 1965-8.
Parlitz U, Berg S, Luther S, Schirdewan A, Kurths J, Wessel N. Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics. Comp. Biol. Med. 2012;42(3):319-27.
Baumert M, Baier V, Truebner S, Schirdewan A, Voss A. Short-and long-term joint symbolic dynamics of heart rate and blood pressure in dilated cardiomyopathy. IEEE Trans. Biomed. Eng. 2005;52(12):2112-5.
Cysarz D, Porta A, Montano N, Van Leeuwen P, Kurths J, Wessel N. Different approaches of symbolic dynamics to quantify heart rate complexity. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013; pp. 5041-4.
Caminal P, Giraldo BF, Vallverdú M, Benito S, Schroeder R, Voss A. Symbolic dynamic analysis of relations between cardiac and breathing cycles in patients on weaning trials. Ann. Biomed. Eng. 2010;38(8):2542-52.
Baumert M, Brown R, Duma S, Broe GA, Kabir MM, Macefield VG. Joint symbolic dynamics as a model-free approach to study interdependence in cardio-respiratory time series. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012; pp. 3680-3.
Kabir MM, Saint DA, Nalivaiko E, Abbott D, Voss A, Baumert M. Quantification of cardiorespiratory interactions based on joint symbolic dynamics. Ann. Biomed. Eng. 2011;39(10):2604-14.
Reulecke S, Schulz S, Bauer R, Witte H, Voss A. Quantification of cardiovascular and cardiorespiratory coupling during hypoxia with Joint Symbolic Dynamics. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011; pp. 2695-8.
Ravelo-García AG, Saavedra-Santana P, Juliá-Serdá G, Navarro-Mesa JL, Navarro-Esteva J, Álvarez-López X, Gapelyuk A, Penzel T, Wessel N. Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening. Chaos. 2014;24(2):024404-1-024404-8.
Kabir MM, Kohler M, Abbott D, Baumert M. Quantification of cardio-respiratory interactions in healthy children during night-time sleep using joint symbolic dynamics. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011; pp. 1459-62.
Caminal P, Mateu J, Vallverdú M, Giraldo B, Benito S, Voss A. Estimating respiratory pattern variability by symbolic dynamics. Meth. Inf. Med. 2004;43(1):22-5.
Caminal P, Vallverdú M, Giraldo B, Benito S, Vázquez G, Voss A. Optimized symbolic dynamics approach for the analysis of the respiratory pattern. IEEE Transact. Biomed. Eng. 2005;52, (11):1832-9.
Baumert M, Walther T, Hopfe J, Stepan H, Faber R, Voss A. Joint symbolic dynamic analysis of beat-to-beat interactions of heart rate and systolic blood pressure in normal pregnancy. Med. Biol. Eng. Comput. 2002;40(2):241-5.
Baranowski R, Zebrowski JJ. Assessment of the RR versus QT relation by a new symbolic dynamics method. Gender differences in repolarization dynamics. Journ. Electrocardiol. 2002;35(2):95-103.
Rothberger CJ. Normale und pathologische physiologie der rhythmik und koordination des herzens. Ergebnisse der Physiologie. 1931;32(1):472-820.
Wessel N, Suhrbier A, Riedl M, Marwan N, Malberg H, Bretthauer G, Penzel T, Kurths J. Symbolic coupling traces for causality analysis of cardiovascular control. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011; pp. 5935-8.
Baier V, Baumert M, Caminal P, Vallverdú M, Faber R, Voss A. Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders. IEEE Trans. Biomed. Eng. 2006;53(1):140-3.
Babloyantz A, Destexhe A. Is the normal heart a periodic oscillator? Biol. Cybern. 1988;58(3):203-11.
Cysarz D, Lange S, Matthiessen PF, Leeuwen P. Regular heartbeat dynamics are associated with cardiac health. Amer. Journ. Physiol. Regul. Integr. Comp. Physiol. 2007;292(1):R368-R372.
Chon KH, Kanters JK, Cohen RJ, Holstein-Rathlou NH. Detection of "noisy" chaos in a time series. Meth. Inf. Med. 1997;36(4-5):294-7.
Lankhaar JW, Rövekamp FA, Steendijk P, Faes TJ, Westerhof BE, Kind T, Vonk-Noordegraaf A, Westerhof N. Modeling the instantaneous pressure-volume relation of the left ventricle: a comparison of six models. Ann. Biomed. Eng. 2009;37(9):1710-26.
Schäfer C, Plesser T, Abel HH. Methodik der Analyse binärer Sequenzen in der Herzfrequenz. Wien Med. Wochenschr. 1995;145(17-18):521-2.
Cysarz D, Bettermann H, Leeuwen P. Entropies of short binary sequences in heart period dynamics. Amer. Journ. Physiol. Heart Circ. Physiol. 2000;278(6):H2163-H2172.
Cysarz D, Van Leeuwen P, Edelhäuser F, Montano N, Porta A. Binary symbolic dynamics classifies heart rate variability patterns linked to autonomic modulations. Comput. Biol. Med. 2012;42(3):313-8.
Kolesnik VD, Mironchikov ET. Decoding of Cyclic Codes. Ohio:Wright-Patterson, AFB; 1973. 393 p.
Ornstein D. Bernoulli shifts with the same entropy are isomorphic. Adv. Math. 1970;4:337-52.
Wolfmann J. New bounds on cyclic codes from algebraic curves. Lecture Notes in Computer Science. 1989;388:47-62.
Diab M. New systolic architectures for cyclic code encoding. Lecture Notes in Computer Science. 1991;539:140-51.
Blahut RE. Cyclic Codes and Related Codes. In: Blahut RE. Algebraic Methods for Signal Processing and Communications Coding (Editor). 2011; pp. 57-69.
Greenough PP, Hill R. Optimal ternary quasi-cyclic codes. Designs, Codes and Cryptography. 1992;2(1):81-91.
Pei JY, Zhang XJ. Quaternary quasi-cyclic codes. Applied Mathematics. 2008;23(3):359-65.
Conan J, Seguin G. Structural properties and enumeration of quasi cyclic codes. Applicable Algebra in Engineering, Communication and Computing. 1993;4(1):25-39.
Feyziyev FG, Babavand AM. Description of decoding of cyclic codes in the class of sequential machines based on the Meggitt theorem. Automatic Control and Computer Sciences. 2012;46(4):164-9.
Seletkov VL. Variants of decoding of unsystematic cyclic codes. Radioelectronics and Communications Systems. 2013;56(5):260-4.
Shah T, Amanullah de Andrade AA. A method for improving the code rate and error correction capability of a cyclic code. Computational and Applied Mathematics. 2013;32(2):261-74.
Piva M, Sala M. A New Bound for Cyclic Codes Beating the Roos Bound. Lecture Notes in Computer Science. 2013;8080:101-12.
Edirisooriya G, Robinson JP. Cyclic code weight spectra and BIST aliasing. Journal of Electronic Testing. 1991;2(2):153-63.
Cammarota C, Rogora E. Spectral and symbolic analysis of heart rate data during the tilt test. Phys. Rev. E: Stat. Nonlin. Soft Mat. Phys. 2006;74(4):0429031-0429034.
Magagnin V, Bassani T, Bari V, Turiel M, Maestri R, Pinna GD, Porta A. Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices. Physiol. Measur. 2011;32(11):1775-86.
Liu YC, Hung CS, Wu YW, Lee YC, Lin YH, Lin C, Lo MT, Chan CC, Ma HP, Ho YL, Chen CH. Influence of non-alcoholic fatty liver disease on autonomic changes evaluated by the time domain, frequency domain, and symbolic dynamics of heart rate variability. PLOS ONE. 2013;8(4):1-6.
Perseguini NM, Takahashi AC, Rebelatto JR, Silva E, Borghi-Silva A, Porta A, Montano N, Catai AM. Spectral and symbolic analysis of the effect of gender and postural change on cardiac autonomic modulation in healthy elderly subjects. Braz. Journ. Med. Biol. Res. 2011;44(1):29-37.
Taşcă C, Stefăneanu L, Vasilescu C. The myocardial microangiopathy in human and experimental diabetes mellitus. (A microscopic, ultrastructural, morphometric and computer-assisted symboliclogic analysis). Endocrinologie. 1986;24(2):59-69.
Saksena S, Craelius W, Hussain SM, Pantopoulos D, Parsonnet V. Intraoperative Spectral Analysis of Ventricular Potentials During Sinus Rhythm and Ventricular Tachycardia. Proc. of VIIth World Symposium on Cardiac Pacing Vienna. 1983; pp. 677-682.
Hongxuan Z, Yisheng Z, Yuhong X, Thakor NV. Pathological analysis of myocardial cell under ventricular tachycardia and fibrillation based on symbolic dynamics. Journ. Med. Eng. Technol. 2001;25(3):112-7.
Saeed M, Mark R. A novel method for the efficient retrieval of similar multiparameter physiologic time series using wavelet-based symbolic representations. Proc. of AMIA -2006 Ann. Symp. pp. 679-83.
Peng CK, Yang AC, Goldberger AL. Statistical physics approach to categorize biologic signals: from heart rate dynamics to DNA sequences. Chaos. 2007;17(1):015115-1 -015115-6.
Porta A, Tobaldini E, Guzzetti S, Furlan R, Montano N, Gnecchi-Ruscone T. Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. Am. Journ. Physiol. Heart Circ. Physiol. 2007;293(1):H702-H708.
Voss A, Schroeder R, Truebner S, Goernig M, Figulla HR, Schirdewan A. Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincare plot analysis in risk stratification in patients with dilated cardiomyopathy. Chaos. 2007;17(1):015120-1 -015120-7.
Tarkiainen TH, Kuusela TA, Tahvanainen KU, Hartikainen JE, Tiittanen P, Timonen KL, Vanninen EJ. Comparison of methods for editing of ectopic beats in measurements of short-term non-linear heart rate dynamics. Clin. Physiol. Funct. Imaging. 2007;27(2):126-33.
Sunita SM, Bhaaskaran VSK, Hegde D, Dhareshwar P. Error Detection and Correction in Embedded Memories Using Cyclic Code. Lecture Notes in Electrical Engineering. 2013;258:109-16.

Еще

Статья научная