Detection of Amplitude Peak and Duration of QRS Electrocardiogram Waves Using Discrete Data

Deteksi Puncak Amplitudo dan Durasi Gelombang QRS Elektrokardiogram Menggunakan Discrete Data

  • Setiawidayat Sabar Universitas WIdyagama Malang
  • Aviv Yuniar Rahman Universitas Widyagama Malang
  • Ratna Hidayati Sekolah Tinggi Ilmu Kesehatan Karya Husada
Keywords: QS duration, peak amplitude, BME, physionet, discrete data

Abstract

In each cycle of the Heart on the Electrocardiogram there are generally P waves as a presentation of Atrial Muscle Depolarization, QRS waves as a presentation of Ventricular Muscle Depolarization and T waves as a presentation of Ventricular Muscle Repolarization. Some types of electrocardiographs only represent wave morphology and some other types of electrocardiographs are equipped with duration and amplitude information but are limited. This limitation of information is calculated manually using small boxes on ecg paper measuring 40 ms for duration and 1 mV for amplitude. The consequences of this manual calculation will require time and accuracy of the calculation results. This study aims to obtain the QRS wave duration along with the amplitude value in each cycle of cardiac examination results. Discrete data from the sampling results of the ECG continuous signal in the maximum filter amplitude to get peak R values. The position of integer peak R with the next peak R is the duration of the cycle. PQRST algorithm is used to obtain peak Q and peak S, so the duration of QS can be obtained by subtracting the position of integer peak S with integer position Q. 10 samples of discrete ecg Sinus Rhythm data from Physionet and 5 samples from ECG-UWG were used in this study. The results showed that all sample data in 3 cycles had a value of QRS duration and peak amplitude values ​​Q, R and S. Peak amplitude R max values ​​and R min physionet sample records were obtained in record 16273 which was 3,485 mV and record 16795 was 0.805 mV. The QRS duration for Bradicardia and Tachicardia is shown in record 16483 which is 40 ms and record 17052 which is 144 ms.

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References

A. C. Guyton and J. E. Hall, Textbook of medical physiology, 11th ed. Philadelphia: Elsevier Saunders, 2006.

J. R. Cameron and J. G. Skofronick, Medical physics. Wiley, 1978.

B. Chia, Cninical Electrocardiography, Third Edition. New Jersey: World Scientific, 2000.

J. T. Hansen and B. M.Koeppen, Netter’s Atlas of Human Physiology, 4th ed. San Antonio: Elsevier, 2018.

P. A. Iaizzo, Handbook of Cardiac Anatomy, Physiology, and Devices, ANSI Z39.48-1984 (American National Standards Institute) Permanence of Paper for Printed Library Materials. Totowa, New Jersey 07512: © 2005 Humana Press Inc., 2005.

D. B. Foster, Twelve-Lead Electrocardiography, Second. London: Springer-Verlag London, 2007.

R. A. Álvarez, A. J. M. Penín, and X. A. V. Sobrino, “A Comparison of Three QRS Detection Algorithms Over a Public Database,” Procedia Technol., vol. 9, pp. 1159–1165, 2013, doi: 10.1016/j.protcy.2013.12.129.

B. de Luna, Basic Electrocardiography, Normal and Abnormal ECG Patterns, First. Massachusetts: Blackwell Futura, 2007.

A. C. Guyton and J. E. Hall, Textbook of Medical Physiology, 11th ed. Mississippi: Elsevier Saundes, 2006.

A. Burguera, “Fast QRS Detection and ECG Compression Based on Signal Structural Analysis,” IEEE J. Biomed. Health Inform., vol. 23, no. 1, pp. 123–131, Jan. 2019, doi: 10.1109/JBHI.2018.2792404.

A. E. Curtin, K. V. Burns, A. J. Bank, and T. I. Netoff, “QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients,” IEEE J. Transl. Eng. Health Med., vol. 6, pp. 1–11, 2018, doi: 10.1109/JTEHM.2018.2844195.

D. Yang and Y. Zhang, “A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform,” MATEC Web Conf., vol. 175, p. 02008, 2018, doi: 10.1051/matecconf/201817502008.

C. J. Deepu and Y. Lian, “A Joint QRS Detection and Data Compression Scheme for Wearable Sensors,” IEEE Trans. Biomed. Eng., vol. 62, no. 1, pp. 165–175, Jan. 2015, doi: 10.1109/TBME.2014.2342879.

M. R. Arefin, K. Tavakolian, and R. Fazel-Rezai, “QRS complex detection in ECG signal for wearable devices,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 5940–5943, doi: 10.1109/EMBC.2015.7319744.

M. Elgendi, A. Mohamed, and R. Ward, “Efficient ECG Compression and QRS Detection for E-Health Applications,” Sci. Rep., vol. 7, no. 1, Dec. 2017, doi: 10.1038/s41598-017-00540-x.

R. He et al., “A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization,” EURASIP J. Adv. Signal Process., vol. 2017, no. 1, Dec. 2017, doi: 10.1186/s13634-017-0519-3.

S. Hamdi, A. Ben Abdallah, and M. H. Bedoui, “Real time QRS complex detection using DFA and regular grammar,” Biomed. Eng. OnLine, vol. 16, no. 1, Dec. 2017, doi: 10.1186/s12938-017-0322-2.

S. Lee, Y. Jeong, D. Park, B.-J. Yun, and K. Park, “Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation,” Sensors, vol. 18, no. 12, p. 4502, Dec. 2018, doi: 10.3390/s18124502.

B. Subramanian, “ECG signal classification and parameter estimation using multiwavelet transform,” Biomed Res, vol. 28, no. 7, p. 7, 2017.

N. Vuong, T. Nguyen, L. D. Tran, and T. Van Huynh, “Detect QRS complex in ECG,” in 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, 2017, pp. 2022–2027, doi: 10.1109/ICIEA.2017.8283170.

M. Yochum, C. Renaud, and S. Jacquir, “Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT,” Biomed. Signal Process. Control, vol. 25, pp. 46–52, Mar. 2016, doi: 10.1016/j.bspc.2015.10.011.

R. Haddadi, E. Abdelmounim, M. El Hanine, and A. Belaguid, “Discrete Wavelet Transform based algorithm for recognition of QRS complexes,” in 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco, 2014, pp. 375–379, doi: 10.1109/ICMCS.2014.6911261.

C. M. Khamhoo, J. Rahul, and M. Sora, “Algorithm for QRS Complex Detection using Discrete Wavelet Transformed,” vol. 10, no. 2, p. 7, 2018.

V. H. Rodriguez, C. Medrano, and I. Plaza, “A Real-Time QRS Complex Detector Based on Discrete Wavelet Transform and Adaptive Threshold as Standalone Application on ARM Microcontrollers,” in 2018 International Conference on Biomedical Engineering and Applications (ICBEA), Funchal, 2018, pp. 1–6, doi: 10.1109/ICBEA.2018.8471741.

S. Bilgin and Z. E. Akin, “Aritmik EKG Sinyallerinde Dayanikli Yeni Bir QRS Yakalama Algoritmasi,” Mühendis. Bilim. Ve Tasar. Derg., pp. 64–73, Mar. 2018, doi: 10.21923/jesd.391625.

S. setiawidayat, D. Sargowo, and S. Sakti, “Using Discrete Data of ECG in the Numerical and Spectral forms,” Int. J. Electr. Comput. Sci. IJECS-IJENS, vol. Vol.15 No.03, Jun. 2015.

P. Sharma, “ANN Based GUI for ECG Classification and Normality Detection,” World Acad. Res. Sci. Eng., vol. vol.3 no.6, pp. 383–385, Jun. 2014.

S. Setiawidayat, D. Sargowo, S. P. Sakti, and S. Andarini, “The Peak of the PQRST and the Trajectory Path of Each Cycle of the ECG 12-Lead Wave,” Indones. J. Electr. Eng. Comput. Sci., vol. 4, no. 1, pp. 169–175, Oct. 2016.

A. L. Goldberger, Clinical Electrocardiography, 7th ed. Mosby, Elsevier, 2006.

S. Setiawidayat and R. Joegijantoro, “Algorithm for the Representation of Parameter Values of Electrocardiogram,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 16, no. 3, p. 1295, Jun. 2018, doi: 10.12928/telkomnika.v16i3.6934.

S. Setiawidayat and R. Joegijantoro, “Algorithm for the Representation of Parameter Values of Electrocardiogram,” Telkomnika, vol. Vol.16, no.3, no. Medical Engineering, p. 8, Jun. 2018, doi: DOI: 10.12928/TELKOMNIKA.v16i3.6934.

S. Setiawidayat and A. Y. Rahman, “New method for obtaining Peak Value R and the duration of each cycle of Electrocardiogram,” in 2018 International Conference on Sustainable Information Engineering and Technology (SIET), Malang, Indonesia, 2018, pp. 77–81, doi: 10.1109/SIET.2018.8693151.

S. Setiawidayat, M. R. Indra, D. Sargowo, and S. Sakti, “Determining The ECG 1 Cycle Wave using Discrete data,” . Vol., p. 8, 2005.

Published
2020-06-20
How to Cite
Sabar, S., Aviv Yuniar Rahman, & Ratna Hidayati. (2020). Detection of Amplitude Peak and Duration of QRS Electrocardiogram Waves Using Discrete Data. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(3), 438 - 446. https://doi.org/10.29207/resti.v4i3.1658
Section
Information Systems Engineering Articles