Prototype of Portable Heart Monitoring System using BITalino

ERWIN SITOMPUL, ANTONIUS SUHARTOMO, FARHAN DARMAWAN, NENDI SUHENDI SYAFEI, ARJON TURNIP

Sari


ABSTRAK

Jantung adalah organ vital yang menuntut perhatian khusus, terutama untuk orang dengan resiko serangan jantung. Bagi orang kategori ini, diperlukan detektor detak jantung yang bekerja secara kontinu dan real-time yang dapat mendeteksi adanya gangguan jantung secara dini. Pada penelitian ini, penulis mengajukan prototipe sistem monitoring jantung portable (PSMJP) dengan menggunakan modul bio-signal BITalino. Hasil pengukuran diproses pada perangkat komputer yang terhubung dengan BITalino melalui transmisi Bluetooth. Suatu program pemroses sinyal dirancang dengan menggunakan Algorithma Hamilton. Tingkat keberhasilan deteksi pada pengujian terhadap sampel EKG mentah dan pengukuran EKG mentah adalah 100%. PSMJP diujikan kepada 15 naracoba untuk kondisi duduk dan kondisi berjalan. PSMJP berfungsi baik pada 29 dari 30 pengukuran, dimana sinyal elektrik dari jantung terbukti dapat diproses dan memberikan hasil akhir berupa fitur-fitur gelombang detak jantung dan laju detak jantung.

Kata kunci: denyut jantung, algoritma Hamilton, BITalino, EKG

 

ABSTRACT


The heart is a vital organ that requires special attention, especially for people with heart attack risk. For people of this category, a heart rate detector that works continuously and in real-time is needed so that heart problems can be detected. In this study, the authors proposed a prototype of a portable heart monitoring system (PPHMS) using the BITalino bio-signal module. The measurement results are processed on a computer device connected to BITalino via Bluetooth transmission. A signal processing program was designed using Hamilton Algorithm. The detection success rate on testing for a raw ECG sample and raw ECG measurement was 100 %. PPHMS was tested on 15 subjects for sitting conditions and walking conditions. PPHMS works well in 29 of the 30 measurements, where electrical signals from the heart are proven to be successfully processed. The final results in the form of heart wave features and heart rate can be provided.

Keywords: heart rate, Hamilton Algorithm, BITalino, ECG


Kata Kunci


heart rate; Hamilton Algorithm; BITalino; ECG

Teks Lengkap:

PDF (English)

Referensi


Ali, N. S., Alyasseri, Z. A., & Abdulmohson, A. (2018). Real-time Heart Pulse Monitoring Technique using wireless sensor network and mobile application. International Journal of Electrical and Computer Engineering (IJECE), 8(6), 5118. doi:10.11591/ijece.v8i6.pp5118-5126.

Alves, A.P., Silva, H, Lourenco, A, & Fred, A. (2013). BITtalino: A Biosignal acquisition system based on the Arduino. Proceedings of the International Conference on Biomedical Electronics and Devices. doi:10.5220/0004243502610264, (pp. 261 – 264).

Cadogan, M. (2022, 30 January). ECG Lead Positioning. Retrieved from https://litfl.com/ecglead-positioning.

Costa, R., Winkert, T., Manhães, A., & Teixeira, J. P. (2021). QRS peaks, P and T waves identification in ECG. Procedia Computer Science, 181, 957-964. doi:10.1016/j.procs.2021.01.252

Dugdale, D. C. (2020, 18 April). A.D.A.M. Medical Encyclopedia. Retrieved from https://medlineplus.gov/ency/imagepages/19612.htm.

Ganesh, E. N. (2019). Health monitoring system using Raspberry Pi and IoT. Oriental Journal of Computer Science and Technology, 12(1), 08-13. doi:10.13005/ojcst12.01.03.

Gondalia, A., Dixitb, D., Parasharc, S., Raghavad, V., & Senguptae, A. (2018). IoT-based Healthcare Monitoring System for War Soldiers using Machine Learning. International Conference on Robotics and Smart Manufacturing (RoSMa2018) Procedia Computer Science, (pp. 1005 – 1013).

Hamilton, P. (2002). Open-source ECG analysis. Computers in Cardiology. doi:10.1109/cic.2002.1166717. (pp. 101 - 104).

Heryan, K., Reklewski, W., Szaflarski, A., Ordowski, M., Augustyniak, P., & Miskowicz, M. (2021). Sensitivity of QRS detection accuracy to detector temporal resolution. 2021 Computing in Cardiology (CinC), (pp. 267 - 270).

Hossain, M. B., Bashar, S. K., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). An accurate QRS complex and P wave detection in ECG signals using complete ensemble empirical mode decomposition with adaptive noise approach. IEEE Access, 7, 128869 - 128880. doi:10.1109/access.2019.2939943.

Mahajan, P., & Kaul, A. (2022). Arduino-based portable ECG and PPG Signal Acquisition System. 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22). doi:10.1109/icetetsip-2254415.2022.9791669, (pp. 1 - 6).

Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering (BME), 32(3), 230-236. doi:10.1109/tbme.1985.325532

Plux – Wireless Biosignals. (2020). Electrocardiography (ECG) Sensor Data Sheet . Retrieved from http://bitalino.com.

Porr, B., & Howell, L. (2019, 6 August). R-peak detector stress test with a new noisy ECG database reveals significant performance differences amongst popular detectors. doi:10.1101/722397. Retrieved from https://www.biorxiv.org/content/10.1101/722397v2.full.pdf

Republik Indonesia, Kementerian Kesehatan. (2019). Laporan Nasional Riskesdas 2018. Retrieved from https://www.litbang.kemkes.go.id.

Stroobandt, R.X., Barold, S.S., & Sinnaeve, A.F. (2016). ECG from Basics to Essentials: Step By Step. Chichester, West Sussex, UK: Wiley-Blackwell.

Toral, V., García, A., Romero, F.J., Morales, D. P., Castillo, E., Parrilla, L., Gómez-Campos, F. M., Morillas, A., & Sánchez, A. (2019). Wearable system for Biosignal acquisition and monitoring based on Reconfigurable Technologies. Sensors, 19(7), 1590. doi:10.3390/s19071590.

Turnip, A., Kusumandari, D. E., Wijaya, C., Turnip, M., & Sitompul, E. (2019). Extraction of P and T waves from electrocardiogram signals with modified Hamilton algorithm. 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC). doi:10.1109/icsecc.2019.8907016.

Wei, Q., Park, H., & Lee, J. H. (2019). Development of a wireless health monitoring system for measuring core body temperature from the back of the body. Journal of Healthcare Engineering, 2019, 1-8. doi:10.1155/2019/8936121.

Zago, G. T., Andreão, R. V., Rodrigues, S. L., Mill, J. G., & Sarcinelli Filho, M. (2015). ECGbased detection of left ventricle hypertrophy. Research on Biomedical Engineering, 31(2), 125-132. doi:10.1590/2446-4740.0691.




DOI: https://doi.org/10.26760/elkomika.v11i1.31

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License