ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-6-1166-1177ntv-351Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEАвтоматизированная оценка параметров электрокардиограмм в условиях пандемии COVID-19Automated evaluation of ECG parameters during the COVID-19 pandemichttps://orcid.org/0000-0002-5483-716XВатьянА. С.VatianA. S.

Ватьян Александра Сергеевна – кандидат технических наук, доцент

Санкт-Петербург, 197101

sc 57191870868

Alexandra S. Vatian – PhD, Associate Professor

Saint Petersburg, 197101

sc 57191870868

alexvatyan@gmail.comhttps://orcid.org/0000-0002-1361-6037ГусароваН. Ф.GusarovaN. F.

Гусарова Наталия Федоровна – кандидат технических наук, старший научный сотрудник, доцент

Санкт-Петербург, 197101

sc 57162764200

Natalia F. Gusarova – PhD, Senior Researcher, Associate Professor

Saint Petersburg, 197101

sc 57162764200

natfed@list.ruhttps://orcid.org/0000-0001-6206-8033ДобренкоН. В.DobrenkoN. V.

Добренко Наталья Викторовна – кандидат технических наук, доцент

Санкт-Петербург, 197101

sc 56499375200

Natalia V. Dobrenko – PhD, Associate Professor

Saint Petersburg, 197101

sc 56499375200

graziokisa@yandex.ruhttps://orcid.org/0000-0002-0077-794XЗмиевскийД. А.ZmievskyD. A.

Змиевский Данил Александрович – студент

Санкт-Петербург, 197101

Danil A. Zmievsky – Student

Saint Petersburg, 197101

danil.zmievskiy@gmail.comhttps://orcid.org/0000-0002-1006-0408КабышевМ. В.KabyshevM. V.

Кабышев Максим Васильевич – аспирант

Санкт-Петербург, 197101

Maxim A. Kabyshev – PhD Student

Saint Petersburg, 197101

maxk6971@gmail.comhttps://orcid.org/0000-0001-6131-0019ПолеваяТ. А.PolevayaT. A.

Полевая Татьяна Андреевна – программист

Санкт-Петербург, 197101

sc 57193708570

Tatiana A. Polevaya – Software Developer

Saint Petersburg, 197101

sc 57193708570

tanusha2406@gmail.comhttps://orcid.org/0000-0002-9046-2457ТатариноваА. А.TatarinovaA. A.

Татаринова Анна Андреевна – кандидат медицинских наук, старший научный сотрудник, старший научный сотрудник

Санкт-Петербург, 197341

sc 6603195545

Anna A. Tatarinova – PhD (Medicine), Senior Researcher, Senior Researcher

Saint Petersburg, 197341

sc 6603195545

antsvet.18@mail.ruhttps://orcid.org/0000-0003-1886-2867ТомиловИ. В.TomilovI. V.

Томилов Иван Вячеславович – старший лаборант

Санкт-Петербург, 197101

Ivan V. Tomilov – Senior Laboratory Assistant

Saint Petersburg, 197101

ivan-tomilov3@yandex.ruУниверситет ИТМОРоссияITMO UniversityRussian FederationНациональный медицинский исследовательский центр им. В.А. АлмазоваРоссияAlmazov National Medical Research CentreRussian Federation20221912202422611661177Copyright © Ватьян А.С., Гусарова Н.Ф., Добренко Н.В., Змиевский Д.А., Кабышев М.В., Полевая Т.А., Татаринова А.А., Томилов И.В., 20242024Ватьян А.С., Гусарова Н.Ф., Добренко Н.В., Змиевский Д.А., Кабышев М.В., Полевая Т.А., Татаринова А.А., Томилов И.В.Vatian A.S., Gusarova N.F., Dobrenko N.V., Zmievsky D.A., Kabyshev M.V., Polevaya T.A., Tatarinova A.A., Tomilov I.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/351Предмет исследования

Предмет исследования. Рассмотрены алгоритмы оперативной автоматизированной оценки параметров электрокардиограммы в условиях отсутствия специализированного оборудования и профильных специалистов.

Метод

Метод. Электрокардиограмма пациента записана на бумажную ленту, которая сфотографирована на мобильный телефон врача первичного звена и обработана специализированным приложением. С помощью приложения выполнена оцифровка фотографируемого изображения электрокардиограммы, оценка ее основных параметров, а также расчет критериев для дифференциальной диагностики отдельных заболеваний по приближенным формулам. Кроме того, оцифрованное изображение электрокардиограммы передается на сервер и обрабатывается с помощью системы машинного обучения. Основные результаты. Разработаны алгоритмы оцифровки и анализа электрокардиограммы, которые могут быть использованы для оценки ее элементов, важных для диагностики. Средняя ошибка определения положения наиболее сложных (сглаженных) пиков – зубцов P и T – составила не более 0,1 мм. Предложен алгоритм критериального анализа электрокардиограммы для поддержки дифференциальной диагностики острого инфаркта миокарда с подъемом сегмента ST и синдрома ранней реполяризации желудочков, который обеспечил значения точности 0,85 и F-меры 0,74. Представлен альтернативный алгоритм на основе глубокой нейронной сети, который обеспечивает лучшие значения точности и F-меры – 0,96 и 0,88, но требует больших затрат вычислительных ресурсов и выполнения расчетов на сервере.

Практическая значимость

Практическая значимость. Алгоритмы реализованы в виде набора библиотечных функций. Они могут быть использованы как самостоятельно, так и в составе полномасштабной системы поддержки принятия клинических решений для автоматизированной оценки параметров электрокардиограммы на основе клиент-серверной архитектуры. Все результаты расчетов совместно с фотографией исходной электрокардиограммы могут быть оперативно переданы квалифицированному кардиологу с целью дистанционного получения консультативного заключения.

Algorithms for prompt automated evaluation of electrocardiogram parameters in the absence of specialized equipment and specialized specialists are considered. The patient’s electrocardiogram is recorded on a paper tape, then it is photographed on the primary care doctor’s mobile phone and processed by a specialized application. The application digitizes the photographed image of the electrocardiogram, evaluates its main parameters as well as calculates criteria for the differential diagnosis of certain diseases using approximate formulas. In addition, the digitized electrocardiogram image is transmitted to the server and processed using a machine learning system. Algorithms for digitizing and analyzing an electrocardiogram have been developed that make it possible to evaluate its elements that are important for diagnosis, and the average error in determining the position of the most complex (smoothed) peaks – P and T waves – was no more than 0.1 mm. An algorithm for the criteria analysis of an electrocardiogram is proposed to support the differential diagnosis of acute myocardial infarction with ST segment elevation and early ventricular repolarization syndrome which provides accuracy values of 0.85 and F-scores of 0.74. An alternative algorithm based on a deep neural network is proposed which provides the best values – 0.96 and 0.88, respectively, but requires large computing resources and is executed on the server. The algorithms are implemented as a set of library functions. They can be used both independently and as part of a full-scale clinical decision support system for automated evaluation of electrocardiogram parameters based on a client-server architecture. In addition, all calculation results, together with a photograph of the original electrocardiogram, can be promptly transferred to a qualified cardiologist in order to receive an advisory opinion remotely.

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The authors declare that there are no conflicts of interest present.