Modeling the Maps of External Potentials for Studying Electrocardiography Inverse Problem Solution Algorithms
DOI:
https://doi.org/10.24160/1993-6982-2018-3-132-140Keywords:
heart electric field simulation, electrocardiographic maps of external potentials, pathological changes in the myocardium, cellular automataAbstract
The article addresses matters concerned with modeling the electrical activity of a myocardium using cellular automata. An algorithm for calculating the electrocardiographic maps of external potentials on the chest surface, including those observed in case of heart muscle pathologies, is proposed. Pathological changes characteristic for ischemic heart areas are understood to mean the presence of myocardium zones with delayed excitation. A finite length circular cylinder is used as the chest surface model. Two versions of the model are considered. The first version is represented by a conducting cylinder placed in conducting medium (the homogeneous model of a chest and its surrounding space). The second version is represented by a conducting cylinder surrounded by air (the model that takes into account the body-air boundary. The heart surface is represented by a sphere covered with a single layer of cellular automata. In modeling the heart electric field, each cellular automaton is interpreted as a point electric dipole. The dipole moment vector is oriented normally to the heart surface. The dipole moment value is determined by the cellular automaton state at each discrete instant of time in the course of myocardium excitation process. The developed model offers the possibilities to change the heart and chest sizes, heart position in the chest, heart electric axis position, and the size and locations of pathologic areas. Based on the simulation results, the electrocardiographic maps of external potentials were compared in a homogeneous medium and taking into account the body-air boundary for typical chest and heart sizes of an adult human. Relative deviations and correlation coefficients for the maps of external potentials at discrete instants of time of a single cardiocycle are calculated and analyzed. The effect of the body-air boundary and the presence and location of pathological areas in the myocardium on the simulation results is studied. The developed model was used to set up a bank of the electrocardiographic external potential maps necessary for testing the ECG inverse problem solution algorithms, that is, for developing and studying algorithms for reconstructing equivalent spatially distributed heart sources based on the electrocardiographic signals obtained from a multi-electrode ECG system. Application of such algorithms will make it possible to enhance the sensitivity and reliability of electrocardiographic diagnostics, especially in the early heart disease stages.
References
2. Бокерия Л.А. и др. Неинвазивное эндокардиальное картирование желудочков сердца на основе решения обратной задачи электрокардиографии // Вестник аритмологии. 2009. № 57. С. 24—28.
3. Клиническая кардиология: диагностика и лечение / под ред. Л.А. Бокерия, Е.З. Голухова. М.: Изд-во НЦССХ им. А.Н. Бакулева, 2011.
4. Крамм М.Н., Стрелков Н.О., Сушок М.В. Погрешности реконструкции параметров токового диполя сердца для неоднородной модели торса человека в виде кругового цилиндра // Журнал радиоэлектроники. 2012. № 12. [Электрон. ресурс] http://jre.cplire.ru/jre/dec12/13/text.html (дата обращения 30.06.2017).
5. Жихарева Г.В., Крамм М.Н. Реконструкция токовых источников сердца в обратной задаче ЭКГ. Saarbrücken: LAP LAMBERT Academic Publishing, 2012.
6. Giffard-Roisin S. e. a. Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping // IEEE Trans. on Biomedical Eng., Institute of Electrical and Electronics Eng. 2017. V. 64 (9). Pp. 2206—2218.
7. Полякова И.П. Поверхностное ЭКГ-картирование и неинвазивная оценка электрофизиологических свойств миокарда у больных с нарушениями ритма сердца // Анналы аритмологии. 2006. № 6. С. 5—23.
8. Daly M.J. e. a. Epicardial Potentials Computed from the Body Surface Potential Map Using Inverse Electrocardiography and an Individualised Torso Model Improve Sensitivity for Acute Myocardial Infarction Diagnosis // The European Soc. of Cardiology. 2016. Pp. 1—8.
9. Schulze W.H.W. ECG Imaging of Ventricular Activity in Clinical Applications. KIT Scientific Publ., 2015.
10. Афшар Э., Жихарева Г.В., Куприянова Я.А. Моделирование испытательных электрокардиографических сигналов при наличии ишемии миокарда // Вестник МЭИ. 2015. № 4. С. 86—91.
11. Крамм М.Н., Стрелков Н.О. Расчет электрических потенциалов, создаваемых дипольным источником в круговом проводящем цилиндре конечной длины // Радиотехника и электроника. 2015. Т. 60. № 2. C. 173—178.
12. Григорьев М.Г., Бабич Л.Н. Анализ методов неинвазивного исследования сердца для решения обратной задачи электрокардиографии // Молодой ученый. 2015. № 10. С. 184—188.
13. Zettinig O. e. a. Data-driven Estimation of Cardiac Electrical Diffusivity from 12-lead ECG Signals // Medical Image Analysis. 2014. V. 18 (8). Pp. 1361—1376.
14. Патофизиология. Т. 2. М.: Геоэтар-Медиа, 2009.
15. Мурашко В.В., Струтынский А.В. Электрокардиография. М.: МЕДпресс-информ, 2016.
16. Полякова И.П., Феофанова Т.Б., Богданов А.Р., Дербенева С.А. Ранняя неинвазивная диагностика ишемической болезни сердца у пациента с метаболическим синдромом, морбидным ожирением и сопутствующими нарушениями внутрижелудочкового проведения // Креативная кардиология. 2015. № 1. С. 70—79.
17. Новые методы электрокардиографии / под ред. С.В. Грачева, Г.Г. Иванова, А.Л. Сыркина. М.: Техносфера, 2007.
---
Для цитирования: Куприянова Я.А., Жихарева Г.В., Маралкина Е.П., Стрелков Н.О. Моделирование карт наружных потенциалов для исследования алгоритмов решения обратных задач электрокардиографии // Вестник МЭИ. 2018. № 3. С. 132—140. DOI: 10.24160/1993-6982-2018-3-132-140.
#
1. Titomir L.I. i dr. Biofizicheskie Osnovy Elektrokardiograficheskikh Metodov. M.: Fizmatlit, 2009. (in Russian).
2. Bokeriya L.A. i dr. Neinvazivnoe Endokardial'noe Kartirovanie Zheludochkov Serdtsa na Osnove Resheniya Obratnoy Zadachi Elektrokardiografii. Vestnik Aritmologii. 2009;57:24—28. (in Russian).
3. Klinicheskaya Kardiologiya: Diagnostika i Leche- nie / pod red. L.A. Bokeriya, E.Z. Golukhova. M.: Izd-vo NTSSSKH im. A.N. Bakuleva, 2011. (in Russian).
4. Kramm M.N., Strelkov N.O., Sushok M.V. Pogreshnosti Rekonstruktsii Parametrov Tokovogo Dipolya Serdtsa dlya Neodnorodnoy Modeli Torsa Cheloveka v Vide Krugovogo Tsilindra. Zhurnal Radioelektroniki. 2012;12. [Elektron. Resurs] http://jre.cplire.ru/jre/ dec12/13/text.html (Data Obrashcheniya 30.06.2017). (in Russian).
5. Zhikhareva G.V., Kramm M.N. Rekonstruktsiya Tokovykh Istochnikov Serdtsa v Obratnoy Zadache EKG. Saarbrücken: LAP LAMBERT Academic Publishing, 2012. (in Russian).
6. Giffard-Roisin S. e. a. Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping. IEEE Trans. on Biomedical Eng., Institute of Electrical and Electronics Eng. 2017;64 (9):2206—2218.
7. Polyakova I.P. Poverkhnostnoe EKG-kartirovanie i Neinvazivnaya Otsenka Elektrofiziologicheskikh Svoystv Miokarda u Bol'nykh s Narusheniyami Ritma Serdtsa. Annaly Aritmologii. 2006;6:5—23. (in Russian).
8. Daly M.J. e. a. Epicardial Potentials Computed from the Body Surface Potential Map Using Inverse Electrocardiography and an Individualised Torso Model Improve Sensitivity for Acute Myocardial Infarction Diagnosis. The European Soc. of Cardiology. 2016:1—8.
9. Schulze W.H.W. ECG Imaging of Ventricular Activity in Clinical Applications. KIT Scientific Publ., 2015.
10. Afshar E., Zhikhareva G.V., Kupriyanova Ya.A. Modelirovanie Ispytatel'nykh Elektrokardiograficheskikh Signalov pri Nalichii Ishemii Miokarda. Vestnik MPEI. 2015;4:86—91. (in Russian).
11. Kramm M.N., Strelkov N.O. Raschet Elektricheskikh Potentsialov, Sozdavaemykh Dipol'nym Istochnikom v Krugovom Provodyashchem Tsilindre Konechnoy Dliny. Radiotekhnika i Elektronika. 2015;60;2:173—178. (in Russian).
12. Grigor'ev M.G., Babich L.N. Analiz Metodov Neinvazivnogo Issledovaniya Serdtsa dlya Resheniya Obratnoy Zadachi Elektrokardiografii. Molodoy Uchenyy. 2015;10:184—188. (in Russian).
13. Zettinig O. e. a. Data-driven Estimation of Cardiac Electrical Diffusivity from 12-lead ECG Signals. Medical Image Analysis. 2014. V. 18 (8). Pp. 1361—1376.
14. Patofiziologiya. T. 2. M.: Geoetar-Media, 2009. (in Russian).
15. Murashko V.V., Strutynskiy A.V. Elektrokardiografiya. M.: MEDpress-inform, 2016. (in Russian).
16. Polyakova I.P., Feofanova T.B., Bogdanov A.R., Derbeneva S.A. Rannyaya Neinvazivnaya Diagnostika Ishemicheskoy Bolezni Serdtsa u Patsienta s Metabolicheskim Sindromom, Morbidnym Ozhireniem i Soputstvuyushchimi Narusheniyami Vnutrizheludochkovogo Provedeniya. Kreativnaya Kardiologiya. 2015;1: 70—79. (in Russian).
17. Novye Metody Elektrokardiografii / pod red. S.V. Gracheva, G.G. Ivanova, A.L. Syrkina. M.: Tekhnosfera, 2007. (in Russian).
---
For citation: Kuprianova Ya.A., Zhikhareva G.V., Maralkina E.P., Strelkov N.O. Modeling the Maps of External Potentials for Studying Electrocardiography Inverse Problem Solution Algorithms. MPEI Vestnik. 2018;3:132—140. (in Russian). DOI: 10.24160/1993-6982-2018-3-132-140.
