A NEW METHOD FOR THE AUTOMATIC DETECTION OF VENTRICULAR AND ATRIAL PREMATURE CONTRACTIONS

Öz

ECG signals used in the diagnosis of cardiovascular diseases are very important in terms of continuous recording and evaluation during the monitoring of these diseases, determination of appropriate diagnosis and treatment, and observation of possible complications. The most common disturbances among heart diseases are arising from arrhythmias. In this study, it was aimed to detect the cardiac arrhythmias APC and PVC automatically in the computer environment to provide convenience to the physician. In this context, ECG signals were first taken from the MIT-BIH Arrhythmia database and critical points P, Q, R, S, T on the signals were determined. After then, ANN was used for arrhythmia classification as APC, PVC and NSR. It was determined that the best result among the different ANN constructions was obtained with the MLPNN and the accuracy of the test was determined as 99.78% with 3-fold cross-validation and 99.89% with 10-fold cross-validation.

Anahtar Kelimeler

• Electrocardiogram,Atrial Premature Complex,Ventricular Premature Complex,Artificial Neural Networks

KARINCIK VE KULAKÇIK ERKEN VURULARININ OTOMATİK TESPİTİNE DAYALI YENİ BİR YAKLAŞIM

Öz

Kalp-damar hastalıklarının tanısında kullanılan Elektrokardiyogram (EKG) işaretleri, bu hastalıklarının izlenmesi sürecinde sürekli olarak kaydedilip değerlendirilmeleri, uygun tanı ve tedavinin belirlenmesi ve oluşabilecek komplikasyonların gözlemlenmesi açısından oldukça önem taşımaktadır. Kalp hastalıkları arasında en sık karşılaşılan rahatsızlıklar, aritmilerden kaynaklanmaktadır. Bu çalışmada, kalp aritmilerinden olan Erken Kulakçık Vurusu (APC) ve Erken Karıncık Vurusunu (PVC) bilgisayar ortamında otomatik tespit ederek hekime kolaylık sağlamak hedeflenmiştir. Bu kapsamda, ilk olarak MIT-BIH Aritmi veri tabanından EKG sinyalleri alınmış ve sinyaller üzerinde bulunan P, Q, R, S, T kritik noktaları tespit edilmiştir. Sonrasında, Yapay Sinir Ağları (YSA) kullanılarak APC, PVC ve Normal Sinüs Ritmi (NOR) olarak aritmi sınıflandırılması yapılmıştır. Farklı YSA yapıları arasında en iyi sonucun Çok Katmanlı Algılayıcı (ÇKA) ile elde edildiği görülmüş ve sınıflandırmada test doğruluğunun 3 katlı çapraz doğrulama ile %99.78, 10 katlı çapraz doğrulama ile de %99.89 olduğu belirlenmiştir.  

Anahtar Kelimeler

• Elektrokardiyogram,Erken Kulakçık Vurusu,Erken Karıncık Vurusu,Yapay Sinir Ağları

Kaynakça

1. Akin, Z.E., Bilgin, S. (2017). Classification of normal beat, atrial premature contraction and ventricular premature contraction based on discrete wavelet transform and artificial neural networks, in: Medical Technologies National Congress (TIPTEKNO), pp. 1-4. DOI: 10.1109/TIPTEKNO.2017.8238027
2. Akın, Z.E. (2018). Automatic Detection of Ventricular and Atrial Premature Contractions, Akdeniz University, MSc thesis.
3. Alajlan, N., Bazi, Y., Melgani, F., Malek, S., & Bencherif, M. A. (2014). Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods. Signal, Image and Video Processing, 8(5), 931-942.
4. Al Rahhal, M. M., Al Ajlan, N., Bazi, Y., Al Hichri, H., & Rabczuk, T. (2018, May). Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal. In 2018 IEEE International Conference on Electro/Information Technology (EIT) (pp. 0169-0173). IEEE.
5. Anonymous. https://www.physionet.org/physiobank/database/mitdb/ (accessed: 29 July 2018)
6. Bilgin, S., Akin, Z.E. (2018). A New Robust QRS Detection Algorithm in Arrhythmic ECG Signals, Journal of Engineering Sciences and Design, 6 (1), 64-73. DOI: 10.21923/jesd.391625
7. Chetan, A., Tripathy, R.K., Dandapat, S. (2018). A diagnostic system for detection of atrial and ventricular arrhythmia episodes from electrocardiogram, Journal of Medical and Biological Engineering, 38 (2), 304-315. DOI: 10.1007/s40846-017-0294-5
8. Chen, S., Hua, W., Li, Z., Li, J., Gao, X. (2017). Heartbeat classification using projected and dynamic features of ECG signal, Biomed. Signal Process. Control, 31, 165-173. DOI: 10.1016/j.bspc.2016.07.010

9. Chiu, C.C., Lin, T.H., Liau, B.Y. (2005). Using Correlation Coefficient In Ecg Waveform For Arrhythmia Detection, Biomedical Engineering: Applications, Basis and Communications, 17 (03), 147-152. DOI: 10.4015/S1016237205000238
10. De Chazal, P., O’Dwyer, M., Reilly, R.B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng. 51 (7), 1196–1206. DOI: 10.1109/TBME.2004.827359
11. Elgendi, M., Eskofier, B., Dokos, S., Abbott, D. (2014). Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems, PloS one, 9 (1), e84018. DOI: 10.1371/journal.pone.0084018
12. Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals, Comput. Methods Programs Biomed. 127, 52-63. DOI: 10.1016/j.cmpb.2015.12.024
13. Fossa, A.A., Wisialowski, T., Crimin, K. (2006). QT prolongation modifies dynamic restitution and hysteresis of the beat-to-beat QT-TQ interval relationship during normal sinus rhythm under varying states of repolarization, J. Pharmacol. Exp. Ther. 316 (2), 498-506. DOI: 10.1124/jpet.105.095471
14. García, A., Romano, H., Laciar, E., Correa, R. (2011). Development of an algorithm for heartbeats detection and classification in Holter records based on temporal and morphological features, in: Journal of Physics: Conference Series, 332 (1), 012023. DOI: 10.1088/1742-6596/332/1/012023
15. Goetz, T. (2010). The Decision Tree: Taking Control of Your Health in the New Era of Personalized Medicine, New York, NY, USA: Rodale.
16. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.Ch., Mark, R.G, Mietus, J.E., Moody, G.B., Peng, C-K., Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, Circulation, 101 (23), e215-e220.
17. Goldberger, A.L. (2006). Clinical electrocardiography: A simplified approach, 7, Mosby Elsevier.
18. Goutas, A., Ferdi, Y., Herbeuval, J.P., Boudraa, M., Boucheham, B., (2005). Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation, ITBM-RBM, 26 (2), 127–132. DOI: 10.1016/j.rbmret.2004.11.022
19. Hajeb-Mohammadalipour, S., Ahmadi, M., Shahghadami, R., Chon, K. (2018). Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals, Sensors, 18 (7), DOI: 10.3390/s18072090
20. Hayden, G., Brady, W., Perron, A., Somers, M., Mattu, A. (2002). Electrocardiographic T-wave inversion: Differential diagnosis in the chest pain patient, Am. J. Emerg. Med. 20 (3), 252–262. DOI: 10.1053/ajem.2002.32629
21. Hu, H.Y., Hwang, J.N. (2002). Handbook of Neural Network Signal Processing, New York, NY, USA: CRC Press.
22. Jovic, A., Jovic, F. (2017). Classification of cardiac arrhythmias based on alphabet entropy of heart rate variability time series, Biomed. Signal Process. Control, 31, 217-230. DOI: 10.1016/j.bspc.2016.08.010
23. Kaya, Y., & Pehlivan, H. (2015). Classification of premature ventricular contraction in ECG. Int J Adv Comput Sci Appl, 6(7), 34-40.
24. Krasteva, V.T., Jekova, I.I., Christov, I.I., (2006). Automatic detection of premature atrial contractions in the electrocardiogram, Electrotechniques Electronics E & E.
25. Kumar, M., Pachori, R.B., Acharya, U.R. (2017). Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals, Biomed. Signal Process. Control, 31, 301-308. DOI: 10.1016/j.bspc.2016.08.018
26. Li, T., Ma, J., Pan, X., Zhai, Y., Man, K.L. (2017). Classification of Arrhythmia using Multi-Class Support Vector Machine, in Proceedings of the International MultiConference of Engineers and Computer Scientists.
27. Looney, C.G., 1996. Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans. Knowledge Data Eng. 8 (2), 211–226.
28. Luz, E.J.D.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey, Comput. Methods Programs Biomed. 127, 144-164. DOI: 10.1016/j.cmpb.2015.12.008
29. Martis, R.J., Acharya, U.R., Mandana, K.M., Ray, A.K., Chakraborty, C. (2012). Application of principal component analysis to ECG signals for automated diagnosis of cardiac health, Expert Syst. Appl. 39 (14), 11792–11800. DOI: 10.1016/j.eswa.2012.04.072
30. Michael, S., Brady, W., Perron, A., Mattu, A. (2002). The prominent T wave: Electrocardiographic differential diagnosis, Am. J. Emerg. Med. 20 (3), 243–251. DOI: 10.1053/ajem.2002.32630
31. Moody, G.B., Mark, R.G. (1990). The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it, in: Computers in Cardiology, Proceedings, 17, 185-188. DOI: 10.1109/CIC.1990.144205
32. Moody, G.B., Mark, R.G. (2001). The impact of the MIT-BIH arrhythmia database, IEEE Eng. Med. Biol. Mag. 20 (3), 45–50. DOI: 10.1109/51.932724
33. Nair, A., Marziliano, P. (2014). P and T wave detectionon multichannel ECG using FRI, in: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2269–2273. DOI: 10.1109/EMBC.2014.6944072
34. Niederjohn, R. (1975). A mathematical formulation and comparison of zero-crossing analysis techniques which have been applied to automatic speech recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing 23 (4), 373-380. DOI: 10.1109/TASSP.1975.1162702
35. Polat, K., Güneş, S. (2007). Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine, Appl. Math. Comput. 186 (1), 898–906. DOI: 10.1016/j.amc.2006.08.020
36. Raj, S., Ray, K.C. (2018). Sparse representation of ECG signals for automated recognition of cardiac arrhythmias, Expert Systems with Applications 105, 49-64. DOI: 10.1016/j.eswa.2018.03.038
37. Ravindra, K.N., Dhuli, R. (2017). Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine, Computers in biology and medicine, 87, 271-284. DOI: 10.1016/j.compbiomed.2017.06.006
38. Scholz, U.J., Bianchi, A.M., Cerutti, S., Kubicki, S. (1997). Vegetative background of sleep: Spectral analysis of the heart rate variability, Physıol. Behav. 62 (5) 1037–1043. DOI: 10.1016/S0031-9384(97)00234-5
39. Shouldice, R., O’Brien, L., O’Brien, C., De Chazal, P., Gozal, D., Heneghan, C. (2004). Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features, Sleep, 27 (4), 784–792. DOI: 10.1093/sleep/27.4.784
40. Smith, D., Nowacki, D., Li, J.J. (2010). ECG T-Wave Monitor for Potential Early Detection and Diagnosis of Cardiac Arrhythmias, Cardıovasc. Eng. 10 (4), 201–206. DOI: 10.1007/s10558-010-9106-z
41. Specht, D. (1991). A general regression neural network, IEEE Trans. Neural Netw. 2, 568-576. DOI: 10.1109/72.97934
42. Sun, Y., Chan, K.L., Krishnan, S.M. (2005). Characteristic wave detection in ECG signal using morphological transform, BMC Cardiovasc. Disord. 5(1), 28. DOI: 10.1186/1471-2261-5-28
43. Tran, T., McNames, J., Aboy, M., Goldstein, B. (2004). Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes, IEEE Trans. Biomed. Eng. 51 (4), 561–569. DOI: 10.1109/TBME.2003.821030
44. Trinder, J., Kleiman, J., Carrington, M., Smith, S., Breen, S., Tan, N., Kim, Y. (2001). Autonomic activity during human sleep as a function of time and sleep stage, J. Sleep Res. 10 (4), 253–264. DOI: 10.1046/j.1365-2869.2001.00263.x
45. Tsipouras, M.G., Fotiadis, D.I., Sideris, D. (2002). Arrhythmia classification using the RR-interval duration signal, in: Comput. Cardiol. pp. 485–488. DOI: 10.1109/CIC.2002.1166815
46. United Nations, (2015). Department of economic and social affairs population division, World population aging 2015. New York.
47. Vazquez-Seisdedos, C., Neto, J., Maranon Reyes, E., Klautau, A., De Oliveira, R.C.L. (2011). New approach for T-wave end detection on electrocardiogram: Performance in noisy conditions, Bıomed. Eng. Onlıne, 10 (1), 77. DOI: 10.1186/1475-925X-10-77
48. Yang, T., Yu, L., & He, B. (2017). Localization of Premature Ventricular Contractions Using Convolutional Neural Network From 12-lead Electrocardiogram. Circulation, 136(suppl_1), A17031-A17031.
49. Wan, X., Xu, D. (2010). An ECG T waves detection scheme based on the compensatory criterion, in: Proceedings of the 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pp. 730–734. DOI: 10.1109/BMEI.2010.5640074
50. Zapanta, L., Poon, C., White, D., Marcus, C., Katz, E. (2004). Heart rate chaos in obstructive sleep apnea in children, in: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3889–3892. DOI: 10.1109/IEMBS.2004.1404088