Обзор методов автоматической диагностики сердечной аритмии для принятия решений о необходимости проведения дефибрилляции

D. A. Lipchak, A. A. Chupov

Аннотация


Фибрилляция желудочков сердца считается наиболее часто встречающейся причиной внезапной остановки сердца. Такая фибрилляция и часто предшествующая ей желудочковая тахикардия – это ритмы сердца, которые могут реагировать на экстренную электрошоковую терапию и вернуться к нормальному синусовому ритму при ранней диагностике после остановки сердца с восстановлением адекватной насосной функции сердца. Однако ручная проверка сигналов ЭКГ на наличие паттерна такой аритмии является сложной аналитической задачей, требующей немедленного принятия решения в стрессовой ситуации, практически невыполнимой в отсутствие квалифицированного медицинского специалиста. Поэтому для автоматической диагностики острых состояний широкое применение получили системы компьютерной классификации аритмий с функцией принятия решения о необходимости проведения электрокардиотерапии с параметрами высоковольтного импульса, вычисленного адаптивно для каждого пациента. В данной работе рассмотрены методы анализа электрокардиографического сигнала, снимаемого с электродов наружного автоматического или полуавтоматического дефибриллятора, с целью принятия решения о необходимости оказания дефибрилляции, применимые во встроенном программном обеспечении автоматических и полуавтоматических внешних дефибрилляторов. Работа включает обзор применимых методов фильтрации, а также последующих алгоритмов извлечения, классификации и сжатия характерных признаков для сигнала ЭКГ. 

 

Липчак Д. А., Чупов А. А. Обзор методов автоматической диагностики сердечной аритмии для принятия решений о необходимости проведения дефибрилляции. Ural Radio Engineering Journal. 2021;5(4):380–409. DOI: 10.15826/urej.2021.5.4.004.

 

Ключевые слова


аритмия; цифровая обработка сигнала; фильтрация; машинное обучение; автоматический наружный дефибриллятор; дефибрилляция

Полный текст:

PDF (English)

Литература


Gurvich N. L. Basic principles of cardiac defibrillation. Moscow: Medicine; 1977. 180 p. (In Russ.)

Diack A. W., Welborn W. S., Rullman R. G., Walter C. W., Wayne M. A. An automatic cardiac resuscitator for emergency treatment of cardiac arrest. Medical Instrumentation. 1979;13(2):78–83. PMID: 431428.

Bagnenko S. F., Vertkin A. L., Miroshnichenko A. G., Khabutia M. Sh. Emergency Medical Guide. Moscow: GEOTAR-Media; 2006. (In Russ.)

Overview of updated American Heart Association recommendations for CPR and emergency care for cardiovascular diseases from 2015. (In Russ.) Available at: https://association-ar.ru/wp-content/uploads/2019/04/3Obnovlennye-klinicheskie-rekomendacii-Amerikanskoj-Associacii-Serdcapo-legochnoj-reanimacii.pdf

Clinical Practice Guidelines: Cardiac/Cardiac arrest, Version February 2015, Queensland Government.

European Resuscitation Council Guidelines for Resuscitation 2010. Neumar R. W., Shuster M., Callaway C. W., et al. Part 1: executive summary: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015; 132(18 Suppl 2):S315–367. DOI: 10.1161/CIR.0000000000000252

Hazinski M. F., Nolan J. P., Aicken R., et al. Part 1: executive summary: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. Circulation. 2015;132(16 Suppl 1):S2–39. DOI: 10.1161/CIR.0000000000000270

Warner W. D., Cobb L. A., Dennis D., et al. Amplitude of ventricular fibrillation waveform and outcome after cardiac arrest. Annals of Internal Medicine. 1985;102(1):53–55. DOI: 10.7326/0003-4819-102-1-53

Mattioni T. A., Nademanee K., Brodsky M., et al. Initial clinical experience with a fully automatic in-hospital external cardioverter defibrillator. PACE. 1999:22(11):1648–1655. DOI: 10.1111/j.15408159.1999.tb00385.x

Zhang X.-S., Lin D. System and method for complexity analysisbased cardiac tachyarrhythmia detection. USA, 2002, patent No. 6490478.

Kerber R. E., Becker L. B., Bourland J. D., et al. Automatic external defibrillators for public access defibrillation: recommendations for specifying and reporting arrhythmia analysis algorithm performance, incorporating new waveforms, and enhancing safety. Circulation. 1997;95(6):1677–1682. DOI: 10.1161/01.cir.95.6.1677

Flores N., Avitia R. L., Reyna M. A., Garcí C. Readily available ECG databases. Journal of Electrocardiology. 2018;51(6):1095–1097. DOI: 10.1016/j.jelectrocard.2018.09.012

Nguyen M. T., Shahzad A., Van Nguyen B., Kim K., Diagnosis of shockable rhythms for automated external defibrillators using a reliable support vector machine classifier. Biomedical Signal Processing and Control. 2018;44:258–270. DOI: 10.1016/j.bspc.2018.03.014

Cheng P., Dong X. Life-threatening ventricular arrhythmia detection with personalized features. IEEE Access. 2017;5:14195–14203. DOI: 10.1109/ACCESS.2017.2723258

Nguyen M. T., Van Nguyen B., Kim K. Shockable rhythm diagnosis for automated external defibrillators using a modified variational mode decomposition technique. IEEE Transactions on Industrial Informatics. 2017;13(6):3037–3046. DOI: 10.1109/TII.2017.2740435

Tripathy R. K., Sharma L. N., Dandapat S. Detection of shockable ventricular arrhythmia using variational mode decomposition. Journal of Medical Systems. 2016;40(4):79. DOI: 10.1007/s10916-016-0441-5

Figuera C., Irusta U., Morgado E., Aramendi E., Ayala U., Wik L., Kramer-Johansen J., Eftestøl T., Alonso-Atienza F., Machine learning techniques for the detection of shockable rhythms in automated external defibrillators. PLoS One. 2016;11. DOI: 10.1371/journal.pone.0159654

Oh S. L., Hagiwara Y., Adam M., Sudarshan V. K., Koh J. E., Tan J. H., Chua C. K., Tan R. S., Ng E. Y. K. Shockable versus nonshockable life-threatening ventricular arrhythmias using dwt and nonlinear features of ECG signals. Journal of Mechanics in Medicine and Biology. 2017;17(7):1740004. DOI: 10.1142/S0219519417400048

Sharma M., Tan R.-S., Acharya U. R. Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters. Neural Computing and Applications. 2020;32(20):15869–15884. DOI: 10.1007/ s00521-019-04061-8

Sharma M., Singh S., Kumar A., San Tan R., Acharya U. R. Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features. Computers in Biology and Medicine. 2019;115:103446. DOI: 10.1016/j.compbiomed.2019.103446

Pan J., Tompkis W. J. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985;BME-32(3):230–236. DOI: 10.1109/TBME.1985.325532

Thakor N. V., Zhu Y. S., Pan K. Y. Ventricular Tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Transactions on Biomedical Engineering. 1990;37(9):837–843. DOI: 10.1109/10.58594

Arafat M. A., Chowdhury A. W., Hasan M. K. A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram. Signal, Image and Video Processing. 2011;5(1):1–10. DOI: 10.1007/ s11760-009-0136-1

Anas E., Lee S. Y., Hasan M. K. Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions. BioMedical Engineering OnLine. 2010;9(1):43. DOI: 10.1186/1475-925X-9-43

Amann A., Tratnig R., Unterkofler K. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. BioMedical Engineering OnLine. 2005;4:60. DOI: 10.1186/1475-925X-4-60

Jekova I., Krasteva V. Real time detection of ventricular fibrillation and tachycardia. Physiological Measurement. 2004;25(5):1167–1178. DOI: 10.1088/0967-3334/25/5/007

Irusta U., Ruiz J., Aramendi E., Ruiz de Gauna S., Ayala U., Alonso E. A high-temporal resolution algorithm to discriminate shockable from nonshockable rhythms in adults and children. Resuscitation. 2012;83(9):1090–1097. DOI: 10.1016/j.resuscitation.2012.01.032

Ayala U., Irusta U., Ruiz J., Eftestøl T., Kramer-Johansen J., Alonso-Atienza F., Alonso E., González-Otero D. A reliable method for rhythm analysis during cardiopulmonary resuscitation. BioMed Research International. 2014:872470. DOI: 10.1155/2014/872470

Kuo S., Dillman R.Computer detection of ventricular fibrillation. IEEE Computers in Cardiology. 1978:347–349.

Barro S., Ruiz R., Cabello D., Mira J. Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. Journal of Biomedical Engineering. 1989;11(4):320–328. DOI: 10.1016/0141-5425(89)90067-8

Dzwonczyk R., Brown C. G., Werman H. A. The median frequency of the ECG during ventricular fibrillation: its use in an algorithm for estimating the duration of cardiac arrest. IEEE Transactions on Biomedical Engineering. 1990;37(6):640–646. DOI: 10.1109/10.55668

Zhdanov A. E. et al. OculusGraphy: Literature Review on Electrophysiological Research Methods in Ophthalmology and Electroretinograms Processing Using Wavelet Transform. In: 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, 29–30 Oct. 2020. IEEE; 2020, pp. 1–6. DOI: 10.1109/EHB50910.2020.9280221

Li Q., Mark R. G., Clifford G. D. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiological Measurement. 2008;29(1):15–32. DOI: 10.1088/0967-3334/29/1/002

Li Y., Bisera J., Weil M. H., Tang W. An algorithm used for ventricular fibrillation detection without interrupting chest compression. IEEE Transactions on Biomedical Engineering. 2012;59(1):78–86. DOI: 10.1109/TBME.2011.2118755

Amann A., Tratnig R., Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Transactions on Biomedical Engineering. 2007;54(1):174–177. DOI: 10.1109/TBME.2006.880909

Zhang X. S., Zhu Y. S., Thakor N. V., Wang Z. Z. Detecting ventricular tachycardia and fibrillation by complexity measure, IEEE Transactions on Biomedical Engineering. 1999;46(5):548–555. DOI: 10.1109/10.759055

Singh V., Gupta A., Sohal J. S., Singh A. A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of agestratified subjects. Medical & Biological Engineering & Computing. 2019;57(3):741–755. DOI: 10.1007/s11517-018-1914-0

Singh R. S., Saini B. S., Sunkaria R. K. Arrhythmia detection based on time–frequency features of heart rate variability and backpropagation neural network. Iran Journal of Computer Science. 2019;2(4):245–257. DOI: 10.1007/s42044-019-00042-1

Lake D. E., Richman J. S., Griffin M. P., Moorman J. R. Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 2002;283(3):R789–R797. DOI: 10.1152/ajpregu.00069.2002

Yin J., Xiao P., Li J., Liu Y., Yan C., Zhang Y. Parameters analysis of sample entropy, permutation entropy and permutation ratio entropy for RR interval time series. Information Processing & Management. 2020;57(5):102283. DOI: 10.1016/j.ipm.2020.102283

Sinha N., Das A. Automatic diagnosis of cardiac arrhythmias based on three stage feature fusion and classification model using DWT. Biomedical Signal Processing and Control. 2020;62:102066. DOI: 10.1016/j.bspc.2020.102066

Acharya U. R., Hagiwara Y., Deshpande S. N., Suren S., Koh J. E. W., Oh S. L., Arunkumar N., Ciaccio E. J., Lim C. M. Characterization of focal EEG signals: a review. Future Generation Computer Systems. 2019;91:290–299. DOI: 10.1016/j.future.2018.08.044

Nikias C. L., Mendel J. M. Signal processing with higher-order spectra. IEEE Signal Processing Magazine. 1993;10(3):10–37. DOI: 10.1109/79.221324

Oppenheim A. V., Verghese G. C. Signals, Systems and Inference, complete notes. Class Notes for 6.011: Introduction to Communication, Control and Signal Processing Spring 2010. Available at: https:// mitocw.ups.edu.ec/courses/electrical-engineering-and-computerscience/6-011-introduction-to-communication-control-and-signalprocessing-spring-2010/readings/MIT6_011S10_notes.pdf (accessed November 29, 2021).

Zbilut J. P., Webber C. L. Embeddings and delays as derived from quantification of recurrence plots. Physics Letters A. 1992;171(3-4):199– 203. DOI: 10.1016/0375-9601(92)90426-M

Eckmann J.-P., Kamphorst S. O., Ruelle D. Recurrence plots of dynamical systems. Europhysics Letters. 1987;4(9):973–977. DOI: 10.1209/0295-5075/4/9/004

Marwan N. A historical review of recurrence plots. The European Physical Journal Special Topics. 2008;164(1):3–12. DOI: 10.1140/epjst/ e2008-00829-1

Blum A. L., Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence. 1997;97(1-2):245–271. DOI: 10.1016/S0004-3702(97)00063-5

Granitto P. M., Furlanello C., Biasioli F., Gasperi F. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems. 2006;83(2):83–90. DOI: 10.1016/j.chemolab.2006.01.007

Ververidis D., Kotropoulos C. Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition. Signal Processing. 2008;88(12):2956–2970. DOI: 10.1016/j.sigpro.2008.07.001

Pławiak P. Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm and Evolutionary Computation. 2018;39:192–208. DOI: 10.1016/j.swevo.2017.10.002

Lehmann E. L., Romano J. P. Testing Statistical Hypotheses. 3rd ed. Springer, n.d. (accessed November 29, 2020).

Fisher R. A. Statistical methods for research workers. In: Kotz S., Johnson N. L. (eds) Breakthroughs in Statistics. Springer; 1992, pp. 66–70. DOI: 10.1007/978-1-4612-4380-9_6

Kira K., Rendell L. A. The Feature Selection Problem: Traditional Methods and a New Algorithm. In: AAAI-92 Proceedings. 1992, pp. 129–134. Available at: https://aaai.org/Papers/AAAI/1992/ AAAI92-020.pdf (accessed November 29, 2020).

Park M. Y., Hastie T. L1 Regularization Path Algorithm for Generalized Linear Models. 2006. Available at: https://hastie.su.domains/Papers/glmpath.jrssb.pdf

Raghavendra U., Rajendra Acharya U., Fujita H., Gudigar A., Tan J. H., Chokkadi S. Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images. Applied Soft Computing. 2016;46:151–161. DOI: 10.1016/j.asoc.2016.04.036

Miotto R., Wang F., Wang S., Jiang X., Dudley J. T. Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics. 2018;19(6):1236–1246. DOI: 10.1093/bib/bbx044

Esteva A., Robicquet A., Ramsundar B., Kuleshov V., DePristo M., Chou K., Cui C., Corrado G., Thrun S., Dean J. A guide to deep learning in healthcare. Nature Medicine. 2019;25(1):24–29. DOI: 10.1038/s41591-018-0316-z

Zhang W. Shift-invariant pattern recognition neural network and its optical architecture. In: Proceedings of Annual Conference of the Japan Society of Applied Physics. 1988.

Lawrence S., Giles C. L., Ah Chung Tsoi, Back A. D. Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks. 1997;8(1):98–113. DOI: 10.1109/72.55419

Graves A., Mohamed A., Hinton G. Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, 26–31 May 2013. IEEE; 2013, pp. 6645–6649. DOI: 10.1109/ICASSP.2013.6638947.

IEC 60601-2-4:2002, Medical electrical equipment – Part 2-4: General requirements for safety– Particular requirements for the safety of cardiac defibrillators.

Kong D., Zhu J., Wu S., Duan C., Lu L., Chen D. A novel IRBFRVM model for diagnosis of atrial fibrillation. Computer Methods and Programs in Biomedicine. 2019;177:183–192. DOI: 10.1016/j. cmpb.2019.05.028

Buscema P. M., Grossi E., Massini G., Breda M., Della F. Torre, Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems. Computer Methods and Programs in Biomedicine. 2020;191:105401. DOI: 10.1016/j.cmpb.2020.105401

Kumar M., Pachori R. B., Rajendra Acharya U., Acharya U. R. Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocybernetics and Biomedical Engineering. 2018;38(3):564–573. DOI: 10.1016/j. bbe.2018.04.004

Islam M. S., Ammour N., Alajlan N., Aboalsamh H., Rhythmbased heartbeat duration normalization for atrial fibrillation detection. Computers in Biology and Medicine. 2016;72:160–169. DOI: 10.1016/j. compbiomed.2016.03.015

Baalman S. W. E. E., Schroevers F. E., Oakley A. J., Brouwer T. F., van der Stuijt W., Bleijendaal H., Ramos L. A., Lopes R. R., Marquering H. A., Knops R. E., de Groot J. R. A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples. International Journal of Cardiology. 2020;316:130–136. DOI: 10.1016/j.ijcard.2020.04.046

Panda R., Jain S., Tripathy R. K., Acharya U. R. Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network. Computers in Biology and Medicine. 2020;124:103939. DOI: 10.1016/j. compbiomed.2020.103939

Acharya U. R., Fujita H., Oh S. L., Raghavendra U., Tan J. H., Adam M., Gertych A., Hagiwara Y. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Generation Computer Systems. 2018;79(3):952–959. DOI: 10.1016/j.future.2017.08.039

Dang H., Sun M., Zhang G., Qi X., Zhou X., Chang Q. A novel deep arrhythmia-diagnosis network for atrial fibrillation classification using electrocardiogram signals. IEEE Access. 2019;7:75577–75590. DOI: 10.1109/ACCESS.2019.2918792

Li Z., Feng X., Wu Z., Yang C., Bai B., Yang Q. Classification of atrial fibrillation recurrence based on a convolution neural network with SVM architecture. IEEE Access. 2019;7:77849–77856. DOI: 10.1109/ACCESS.2019.2920900

Wang J. A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network. Future Generation Computer Systems. 2020;102:670–679. DOI: 10.1016/j. future.2019.09.012