The Duration of the Cycle to Get the P Amplitude on A Discrete Electrocardiogram

Sabar Setiawidayat

Abstract


The P amplitude value for each cycle has not been carried out even though it is related to indications of atrial hypertrophy. The basic interpretation of the maximum P amplitude under normal conditions is 2.5 small squares on electrocardiogram (ECG) paper which is equivalent to 2.5 mV. Apart from these interpretations, an amplitude value is required that corresponds to the amount of depolarization of the atrial muscle cells. The difficulty faced by researchers is the lack of discrete ecg data available for experiments, so it only depends on amplitude data as a function of Physionet output time. An ECG is produced using discrete data but there is no electrocardiograph that displays discrete data yet. This study aims to obtain the P amplitude value based on discrete electrocardiogram data. The cycle duration value obtained from R to R is used to obtain the initial position of the cycle (sc) with the formula RN+1-1.5dR for each cycle. The P amplitude value can be obtained by filtering the maximum amplitude value between the sc and RN positions. The results of research on 10 physionet samples and 10 RSSA samples showed that all samples had an amplitude R, cycle duration and P amplitude value in each cycle.


Keywords


P amplitude; duration; discrete; electrocardiogram

Full Text:

PDF

References


M. A. Serhani, H. T. El Kassabi, H. Ismail, and A. Nujum Navaz, “ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges,” Sensors, vol. 20, no. 6, p. 1796, Mar. 2020, doi: 10.3390/s20061796.

M. Ozimek, J. J. Żebrowski, and R. Baranowski, “Information Flow Between Heart Rhythm, Repolarization, and the Diastolic Interval Series for Healthy Individuals and LQTS1 Patients,” Front. Physiol., vol. 12, p. 611731, Jun. 2021, doi: 10.3389/fphys.2021.611731.

T. Tabassum and M. Islam, “An approach of cardiac disease prediction by analyzing ECG signal,” in 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, Bangladesh, Sep. 2016, pp. 1–5. doi: 10.1109/CEEICT.2016.7873093.

I. Mozos and A. Caraba, “Electrocardiographic predictors of

cardiovascular mortality,” Disease Markers, vol. 2015, no.

, pp. 1–10, July 2015.

J. Soar et al., “European Resuscitation Council Guidelines 2021: Adult advanced life support,” Resuscitation, vol. 161, pp. 115–151, Apr. 2021, doi: 10.1016/j.resuscitation.2021.02.010.

I. Morrison, E. Clark, and P. W. Macfarlane, “Evaluation of the electrocardiographic criteria for left ventricular hypertrophy,” Anadolu Kardiyol Derg, p. 5.

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.

S. Setiawidayat, “Correlation of peak amplitude ECG between

leads based on the condition of the heart,” Clin. Med., vol. 8, no.

, pp. 862–872, Feb. 2021.

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, Aug. 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, Jun. 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, Apr. 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, Jul. 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.

T. M. Rosenthal et al., “Optimal method of measuring the T-peak

to T-end interval for risk stratification in primary prevention,” EP

Eur., vol. 20, no. 4, pp. 698–705, Apr. 2018.

S. Setiawidayat and S. I. Putri, “Filtering Data diskrit Elektrokardiogram untuk Penentuan PQRST dalam satu Siklus,” vol. 8, p. 8, 2016.

B. Khelil, A. Kachouri, M. B. Messaoud, and H. Ghariani, “P Wave Analysis in ECG Signals using Correlation for Arrhythmias Detection,” p. 7.

S. Setiawidayat, Panduan Operasional Elektrokardiograf Diskrit,

st ed.Malang, Indonesia: Literasi Nusantara Abadi, 2021.

Physionet, “https://archive.physionet.org/cgi-bin/atm/ATM.”

S. Setiawidayat, “Improved information on heart examination

results uses a 12-lead discrete electrocardiograph,” Eur. J. Electr.

Eng. Comput. Sci., vol. 4, no. 1, pp. 1–8, Feb. 2020.

S. Setiawidayat and R. Joegijantoro, “Algorithm for the

representation of parameter values of electrocardiogram,”

TELKOMNIKA (Telecommun. Comput. Electron. Control), vol.

, no. 3, p. 1295–1302, Jun. 2018.




DOI: https://doi.org/10.17529/jre.v19i2.31605

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


View My Stats

 

Creative Commons License

Jurnal Rekayasa Elektrika (JRE) is published under license of Creative Commons Attribution-ShareAlike 4.0 International License.