SavedAdd your journal

Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2017 / Cilt: 17 - Sayı: 1

A Fully Automatic Novel Method to Determine QT Interval Based on Continuous Wavelet Transform

Mehmet İŞCAN
Abdurrahman YILMAZ
Cüneyt YILMAZ
Pages
3093–3099
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, the proposed algorithm is a fully automatic method to attain a high performance in the calculation of QT intervals at various ECG signals.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, automatic recognition algorithm is being frequently utilized to extract the information concerning cardiac abnormalities.
📊
Key Findings & Data:The proposed algorithm was tested on MIT-BIH-NSR database given by QT database, then, it yielded the scores 15.17 milliseconds and root-mean-square error of 17.19 milliseconds at silver standard, 19.22 milliseconds and 20.22 milliseconds at gold standard, respectively.
💡
Conclusion & Impact:In conclusion, the proposed algorithm is a fully automatic method to attain a high performance in the calculation of QT intervals at various ECG signals.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Nowadays, automatic recognition algorithm is being frequently utilized to extract the information concerning cardiac abnormalities. In this study, a fully automatic novel method based on the continuous wavelet transform (CWT) was developed for QT intervals in various ECG signals. Especially, the determination of T-wave end is the paramount problem to be solved.The developed method was performed to find the beginning of QRS complexes and the end of Twave. The proposed algorithm was tested on MIT-BIH-NSR database given by QT database, then, it yielded the scores 15.17 milliseconds and root-mean-square error of 17.19 milliseconds at silver standard, 19.22 milliseconds and 20.22 milliseconds at gold standard, respectively. In conclusion, the proposed algorithm is a fully automatic method to attain a high performance in the calculation of QT intervals at various ECG signals.

🔖

Cite This Article

İŞCAN, M., YILMAZ, A., YILMAZ, C. (2022). A Fully Automatic Novel Method to Determine QT Interval Based on Continuous Wavelet Transform. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 17 - Sayı: 1, 3093–3099. https://ajindex.com/articles/543600

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (1)

  1. Goldberg, R. J., Bengtson, J., Chen, Z., Anderson, K. M., Locati, E., & Levy, D.,"Duration of the QT interval and total and cardiovascular mortality in healthy persons (The Framingham Heart Study experience)".,The American journal of cardiology, 67(1), 55-58.,1991 [2] Zhang, Q., Manriquez, A. I., Médigue, C., Papelier, Y., & Sorine, M.,“An algorithm for robust and efficient location of T-wave ends in electrocardiograms”,IEEE Transactions on Biomedical Engineering, 53(12), 2544-2552,2006. [3] Goldenberg, I. L. A. N., Moss, A. J., & Zareba, W. QT interval: how to measure it and what is" normal". Journal of cardiovascular electrophysiology, 17(3), 333-336, (2006). [4] Helfenbein, E. D., Zhou, S. H., Lindauer, J. M., Field, D. Q., Gregg, R. E., Wang, J. J.,. & Michaud, F. P. An algorithm for continuous real-time QT interval monitoring. Journal of electrocardiology, 39(4), S123-S127, (2006). [5] Murray, A., McLaughlin, N. B., Bourke, J. P., Doig, J. C., Furniss, S. S., & Campbell, R. W.,“Errors in manual measurement of QT intervals”,British Heart Journal, 71(4), 386-390, 1994. [6] McLaughlin, N. B., Campbell, R. W. F., & Murray, A.,“Accuracy of automatic QT measurement techniques”. Computers in Cardiology, Proceedings. 863-866. IEEE, 1993. [7] Hayn, D., Kollmann, A., & Schreier, G.,“Automated QT interval measurement from multilead ECG signals”,Computers in Cardiology, 381-384, IEEE, 2006. [8] Clifford, G. D., & Villarroel, M. C.,“Model-based determination of QT intervals”,Computers in Cardiology, 357-360, IEEE, 2006. [9] Xue, Q., & Reddy, S. Algorithms for computerized QT analysis. Journal of electrocardiology, 30, 181-186. (1998). [10] Alexander A. Suarez Leon et al, "Neural Network Approach for T wave End Detection: A comparison of Architectures", Comput. Cardiol., vol. 42, pp. 589-592, 2015. [11] Mehmet İşcan; Cüneyt Yilmaz; Faruk Yı̇ ğı̇ t, " T-wave end pattern classification based on Gaussian mixture model", DOI: 10.1109/SIU.2016.7496149, Pages 1953 - 1956, 2016. [12] Mehmet İşcan, Faruk Yı̇ ğı̇ t, Cüneyt Yilmaz," Heartbeat Pattern Classification Algorithm Based on Gaussian Mixture Model", Inter. Symp. on Med. Meas. and App., Italy,2016. [13] Chesnokov, Y. C., Nerukh, D., & Glen, R. C.,“Individually adaptable automatic QT detector”. Computers in Cardiology,337-340, IEEE, 2006. [14] Li, C., Zheng, C., & Tai, C. Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on biomedical Engineering, 42(1), 21-28., (1995). [15] Chu