Pengujian Instrumen Pendeteksi Kelainan Ritme Jantung Menggunakan Data Fisiologi MIT-BIH
Abstract
MIT-BIH database provides authentic ECG signal data that can be used as a source to test the system with varied type of disorders and duration of observation. MIT-BIH ECG signals are converted to analog signals using 11-bit DAC with 360 Hz frequency conversion. Microcontroller converts the analog signals from the output of the generator using an internal 10-bit ADC with a sampling frequency of 200 Hz. Cardiac abnormalities are then analysed based on data sampling. Abnormal heart rhythms are identified using R peak parameter. By measuring the interval between R peaks, the number of beats per minute (bpm) and the interval variation between R peaks can measured to determine abnormal heart rhythms. Results show that DAC output obtains error range from 6.72 milivolt to 14.58 milivolt, whereas ADC output obtains error range from 1 bit to 2 bit. Statistically, test results show significance values from ideal values are greater than α = 0,05 meaning that there is no significant difference between measured R-R intervals with the original R-R intervals by 95% confidence level. The test method successfully detects multiple type of heart rhythms with category: normal, bradycardia, tachycardia, and irregular.
References
M. Thaler, S. Seigafuse, N. Winter, and B. Rivera, the only EKG book youll ever need, 5th ed. Pennsylvania: Lippincott Williams & Wilkins, 2007, pp. 1–251.
A. Sukoco, “Alat Deteksi Dini dan Mandiri Arythmia,” Jurnal Teknologi dan Manajemen Informatika Universitas Merdeka Malang, vol. 6, no. 3, pp. 494–502, 2008.
S. F. Babiker, L. E. Abdel-khair, and S. M. Elbasheer, “Microcontroller Based Heart Rate Monitor using Fingertip Sensors,” University of Khartoum Engineering Journal, vol. 1, no. 2, pp. 47–51, 2011.
M. Fezari, M. Bousbia-salah, and M. Bedda, “Microcontroller Based Heart Rate Monitor,” The International Arab Journal of Information Technology, vol. 5, no. 4, pp. 153–157, 2008.
M. G. Tsipouras, D. I. Fotiadisa, and D. Sideris, “An arrhythmia classification system based on the RR-interval signal.pdf,” Artificial Intelligence in Medicine, vol. 33, pp. 237–250, 2005.
Y. Yeh and W. Wang, “QRS complexes detection for ECG signal: The Difference Operation Method,” computer methods and programs in biomedicine, vol. 1, pp. 245–254, 2008.
J. Pan and W. J. Tompkins, “Realtime QRS Detection Algorithm,” IEEE Trancsactions on Biomedical Engineering, vol. BME-32, no. 3, pp. 230–236, 1985.
“PhysioBank ATM.” [Online]. Available: http://physionet.org/cgibin/ atm/ATM. [Accessed: 24-Jan-2013].
S. Heath, Embedded Systems Design (2nd Edition). Newnes, 2003, pp. 1–451.
M. Margolis, Arduino Cookbook, 1st ed. Gravenstein Highway North, Sebastopol, CA 95472: O’Reilly Media, Inc., 2011, pp. 1–658.
B. Evans, Begining Arduino Programming. Springer Science & Business Media, 2011, pp. 1–271.
“Arduino - Products.” [Online]. Available: http://arduino.cc/en/Main/Products. [Accessed: 24-Jan-2013].
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