LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,051
تعداد مقالات9,665
تعداد مشاهده مقاله12,794,200
تعداد دریافت فایل اصل مقاله6,126,601
Utomo, Trio Pambudi, Nuryani, Nuryani, Nugroho, Anto Satriyo. (1400). A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone. سامانه مدیریت نشریات علمی, 11(5), 641-652. doi: 10.31661/jbpe.v0i0.1912-1017
Trio Pambudi Utomo; Nuryani Nuryani; Anto Satriyo Nugroho. "A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone". سامانه مدیریت نشریات علمی, 11, 5, 1400, 641-652. doi: 10.31661/jbpe.v0i0.1912-1017
Utomo, Trio Pambudi, Nuryani, Nuryani, Nugroho, Anto Satriyo. (1400). 'A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone', سامانه مدیریت نشریات علمی, 11(5), pp. 641-652. doi: 10.31661/jbpe.v0i0.1912-1017
Utomo, Trio Pambudi, Nuryani, Nuryani, Nugroho, Anto Satriyo. A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone. سامانه مدیریت نشریات علمی, 1400; 11(5): 641-652. doi: 10.31661/jbpe.v0i0.1912-1017

A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone

Journal of Biomedical Physics and Engineering
مقاله 10، دوره 11، شماره 5، آذر و دی 2021، صفحه 641-652    اصل مقاله (1.99 M)
نوع مقاله: Technical Note
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1912-1017
نویسندگان
Trio Pambudi Utomo1؛ Nuryani Nuryani* 2؛ Anto Satriyo Nugroho3
1MSc, Department of Physics, Graduate Program, University Sebelas Maret Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
2PhD, Department of Physics, University Sebelas Maret Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
3PhD, Center for Information and Communication Technology Agency for Assessment and Application of Technology, Indonesia
چکیده
QT-interval prolongation is an important parameter for heart arrhythmia diagnosis. It is the time interval from QRS-onset to the T-end of electrocardiogram (ECG). Manual measurement of QT-interval, especially for 12-leads ECG, is time-consuming. Hence, an automatic QT-interval measurement is necessary. A new method for automatic QT-interval measurement is presented in this paper, which mainly consists of three parts, including QRS-complex detection, determination of QRS-onset, and T-end determination. The QRS-complex detection is based on the modified Pan-Tompkins algorithm. The T-end is defined based on Region of Interest (ROI) maximum limit. We compare and test our proposed QT-interval measurement method with reference measurement in term of correlation coefficient and range of 95% LoA. The correlation coefficient and the range of 95% LoA are 0.575 and 0.290, respectively. The proposed method is successfully implemented in ECG monitoring system using smartphone with high performance. The accuracy, positive predictive, and sensitivity of the QRS-complex detection in the system are 99.70%, 99.78%, and 99.92%, respectively. The range of 95% LoA for the comparison between manual and the system’s QT-interval measurement is 0.216. The results show that the proposed method is dependable on the measure of the QT-interval and outperforms the other methods in term of correlation coefficient and range of 95% LoA.
کلیدواژه‌ها
Electrocardiography؛ Smartphone؛ Algorithms؛ Monitoring؛ Physiologic
سایر فایل های مرتبط با مقاله
مراجع
  1. Shaikh MU, Ahmad SA, Adnan WA. Investigation of Data Encryption Algorithm for Secured Transmission of Electrocardiograph (ECG) Signal. IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES); Sarawak, Malaysia, Malaysia: IEEE; 2018. p. 274-8. doi: 10.1109/IECBES.2018.8626640.
  2. Nuryani SL, Nguyen HT. Electrocardiographic T-wave peak-to-end interval for hypoglycaemia detection. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:618-21. doi: 10.1109/IEMBS.2010.5627430.
  3. Ashtiyani M, Navaei Lavasani S, Asgharzadeh Alvar A, Deevband MR. Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm. J Biomed Phys Eng. 2018;8(4):423-34. PubMed PMID: 30568932. PubMed PMCID: PMC5690059.
  4. Anderson HN, Bos JM, Haugaa KH, Morlan BW, Tarrell RF, Caraballo PJ, Ackerman MJ. Prevalence and outcome of high-risk QT prolongation recorded in the emergency department from an institution-wide QT alert system. The Journal of Emergency Medicine. 2018;54(1):8-15. doi: 10.1016/j.jemermed.2017.08.073.
  5. Niemeijer MN, Van Den Berg ME, Eijgelsheim M, Rijnbeek PR, Stricker BH. Pharmacogenetics of drug-induced QT interval prolongation: an update. Drug Safety. 2015;38(10):855-67. doi: 10.1007/s40264-015-0316-6.
  6. Funck-Brentano C, Ouologuem N, Duparc S, Felices M, Sirima SB, Sagara I, Soulama I, Ouédraogo JB, Beavogui AH, Borghini-Fuhrer I, Khan Y. Evaluation of the effects on the QT-interval of 4 artemisinin-based combination therapies with a correction-free and heart rate-free method. Scientific Reports. 2019;9(1):1-8. doi: 10.1038/s41598-018-37113-5.
  7. Zong W, Saeed M, Heldt T. A QT interval detection algorithm based on ECG curve length transform. 2006 Computers in Cardiology; Valencia, Spain: IEEE; 2006. p. 377-80.
  8. Wu CY, Chiu HW. A semi-automatic QT interval measurement based on digital filters. 2006 Computers in Cardiology; Valencia, Spain: IEEE; 2006. p. 317-20.
  9. Chudacek V, Huptych M, Novak D, Lhotska L. Automatic QT interval measurement using rule-based gradient method. 2006 Computers in Cardiology; Valencia, Spain: IEEE. 2006. p. 349-51.
  10. Kasamaki Y, Ozawa Y, Ohta M, Sezai A, Yamaki T, Kaneko M, Watanabe I, Hirayama A, Nakayama T. Automated versus manual measurement of the QT interval and corrected QT interval. Annals of Noninvasive Electrocardiology. 2011;16(2):156-64. doi: 10.1111/j.1542-474X.2011.00423.x.
  11. Panicker GK, Karnad DR, Natekar M, Kothari S, Narula D, Lokhandwala Y. Intra-and interreader variability in QT interval measurement by tangent and threshold methods in a central electrocardiogram laboratory. Journal of Electrocardiology. 2009;42(4):348-52. doi:10.1016/j.jelectrocard.2009.01.003.
  12. Oresko JJ, Jin Z, Cheng J, Huang S, Sun Y, Duschl H, Cheng AC. A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE. 2010;14(3):734-40. doi: 10.1109/TITB.2010.2047865.
  13. Al-Mardini M, Aloul F, Sagahyroon A, Al-Husseini L. Classifying obstructive sleep apnea using smartphones. Journal of Biomedical Informatics. 2014;52:251-9. doi: 10.1016/j.jbi.2014.07.004.
  14. Kakria P, Tripathi NK, Kitipawang P. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. International Journal of Telemedicine and Applications. 2015;2015:1-11. doi: 10.1155/2015/373474.
  15. Hu J, Cui X, Gong Y, Xu X, Gao B, Wen T, Lu TJ, Xu F. Portable microfluidic and smartphone-based devices for monitoring of cardiovascular diseases at the point of care. Biotechnology Advances. 2016;34(3):305-20. doi: 10.1016/j.biotechadv.2016.02.008.
  16. Elgendi M, Eskofier B, Dokos S, Abbott D. Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PloS One. 2014;9(1):e84018. doi: 10.1371/journal.pone.0084018. PubMed PMID: 24409290. PubMed PMCID: PMC3883654.
  17. Smaoui G, Young A, Abid M. Single scale CWT algorithm for ECG beat detection for a portable monitoring system. Journal of Medical and Biological Engineering. 2017;37(1):132-9. doi: 10.1007/s40846-016-0212-2.
  18. Liu SH, Wang JJ, Su CH, Tan TH. Development of a patch-type electrocardiographic monitor for real time heartbeat detection and heart rate variability analysis. Journal of Medical and Biological Engineering. 2018;38(3):411-23. doi: 10.1007/s40846-018-0369-y.
  19. Brucal SG, Guemo JR, Montante SJ, Ng CA, Wong DJ. A Development of a Low-Cost 12-Lead Electrocardiogram Monitoring Device Using Android-based Smartphone. 7th Global Conference on Consumer Electronics (GCCE); Nara, Japan: IEEE; 2018. p. 181-5. doi: 10.1109/GCCE.2018.8574836.
  20. Lin CC, Liou YS, Zhou Z, Wu S. Intelligent Exercise Guidance System Based on Smart Clothing. Journal of Medical and Biological Engineering. 2019;39(5):702-12. doi: 10.1007/s40846-018-0450-6.
  21. Wu T, Redouté JM, Yuce M. A Wearable, Low-Power, Real-Time ECG Monitor for Smart T-shirt and IoT Healthcare Applications. Springer International Publishing; 2019. p. 165-73. doi: 10.1007/978-3-030-02819-0_13.
  22. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215-20. doi: 10.1161/01.CIR.101.23.e215.
  23. Laguna P, Thakor NV, Caminal P, Jane R, Yoon HR, de Luna AB, Marti V, Guindo J. New algorithm for QT interval analysis in 24-hour Holter ECG: performance and applications. Medical and Biological Engineering and Computing. 1990;28(1):67-73. doi: 10.1007/BF02441680.
  24. Pambudi Utomo T, Nuryani N. QRS peak detection for heart rate monitoring on Android smartphone. Journal of Physics Conference Series. 2017;909(1): 012006. doi: 10.1088/1742-6596/909/1/012006.
  25. Sharma T, Sharma KK. QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Computers in Biology and Medicine. 2017;87:187-99. doi: 10.1016/j.compbiomed.2017.05.027.
  26. Varanini M, Tartarisco G, Balocchi R, Macerata A, Pioggia G, Billeci L. A new method for QRS complex detection in multichannel ECG: Application to self-monitoring of fetal health. Computers in Biology and Medicine. 2017;85:125-34. doi: 10.1016/j.compbiomed.2016.04.008.
  27. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985;BME-32(3):230-6. doi: 10.1109/TBME.1985.325532.
  28. Belchandan AK, Deshmukh K, Kumar J. Removal of Noises in ECG Signal by using Digital FIR-IIR Filter in VHDL. Digit Signal Process. 2016;8(5):135-9.
  29. Vishnubhotla R. Pre-processing of ECG signal for ambulatory use. NIU Eng Rev J. 2009;1(2):7-11.
  30. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985;32(3):230-6. doi: 10.1109/TBME.1985.325532.
  31. Miljković N, Popović N, Djordjević O, Konstantinović L, Šekara TB. ECG artifact cancellation in surface EMG signals by fractional order calculus application. Computer Methods and Programs in Biomedicine. 2017;140:259-64. doi: 10.1016/j.cmpb.2016.12.017.
  32. Zhou SH, Helfenbein ED, Lindauer JM, Gregg RE, Feild DQ. Philips QT interval measurement algorithms for diagnostic, ambulatory, and patient monitoring ECG applications. Annals of Noninvasive Electrocardiology. 2009;14(1):S3-8. doi: 10.1111/j.1542-474X.2008.00258.x.
  33. Elgendi M. Fast QRS detection with an optimized knowledge-based method: Evaluation on 11 standard ECG databases. PloS One. 2013;8(9)e73557. doi: 10.1371/journal.pone.0073557. PubMed PMID: 24066054. PubMed PMCID: PMC3774726.
  34. Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D. Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PLoS One. 2013;8(10)e76585. doi: 10.1371/journal.pone.0076585.
  35. Elgendi M, Eskofier B, Abbott D. Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors. 2015;15(7):17693-714. doi: 10.3390/s150717693. PubMed PMID: 26197321. PubMed PMCID: PMC4541954.
  36. Doğan NÖ. Bland-Altman analysis: A paradigm to understand correlation and agreement. Turkish Journal of Emergency Medicine. 2018;18(4):139-41. doi: 10.1016/j.tjem.2018.09.001
  37. Myles PS, Cui JI. Using the Bland–Altman method to measure agreement with repeated measures. BJA. 2007;99(3):309-11. doi: 10.1093/bja/aem214.
آمار
تعداد مشاهده مقاله: 1,143
تعداد دریافت فایل اصل مقاله: 511


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب