Performance Analysis of Twelve Lead ECG Based on Delivery Distance Using Bluetooth Communication

Azel Pralingga  Mukti; Lusiana Lusiana; Dyah Titisari; Satheeshkumar Palanisamy

doi:10.35882/jeeemi.v5i1.275

Azel Pralingga Mukti Departement of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Indonesia
Lusiana Lusiana Departement of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Indonesia
Dyah Titisari Departement of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Indonesia
Satheeshkumar Palanisamy Anna University https://orcid.org/0000-0002-9267-4381

DOI: https://doi.org/10.35882/jeeemi.v5i1.275

Keywords: Heart, Electrocardiograph, Bluetooth Module

Abstract

Based on data from Basic Health Research (Riskesdas) in 2018, the incidence of heart and blood vessel disease is increasing from year to year. At least 15 out of 1000 people or about 2,784,064 individuals in Indonesia suffer from heart disease. Therefore, cardiovascular health care can make a better diagnosis through continuous monitoring. The purpose of this study was to develop a 12-lead circuit, a lead selector (Wilson Central Terminal), an instrumentation booster, an analog filter (Notch Filter 50Hz), Arduino UNO, a Bluetooth module, and Delphi7 application to display electrocardiograph signals. The results show that the Bluetooth module cannot send a signal at a distance of 20 meters if there is no obstacle, cannot send a signal at a distance of 10 meters if there is an obstacle in the form of a wall, and cannot send a signal at a distance of 16 meters if there is an obstacle in the form of wood (doors).

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References

A. M. Shaker, M. Tantawi, H. A. Shedeed, and M. F. Tolba, “Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks,” IEEE Access, vol. 8, pp. 35592–35605, 2020, doi: 10.1109/ACCESS.2020.2974712.

I. Hussain and S. J. Park, “Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management,” IEEE Access, vol. 9, pp. 123146–123164, 2021, doi: 10.1109/ACCESS.2021.3109806.

M. Hammad, P. Pławiak, K. Wang, and U. R. Acharya, “ResNet-Attention model for human authentication using ECG signals,” Expert Syst., vol. 38, no. 6, pp. 1–17, 2021, doi: 10.1111/exsy.12547.

M. Wasimuddin, K. Elleithy, A. S. Abuzneid, M. Faezipour, and O. Abuzaghleh, “Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey,” IEEE Access, vol. 8, pp. 177782–177803, 2020, doi: 10.1109/ACCESS.2020.3026968.

N. Strodthoff, P. Wagner, T. Schaeffter, and W. Samek, “Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 5, pp. 1519–1528, 2021, doi: 10.1109/JBHI.2020.3022989.

B. Campbell, D. Richley, C. Ross, and C. Eggett, “Clinical Guidelines by Consensus. Recording a Standard 12-Lead Electrocardiogram.,” Soc. Cardiol. Sci. & Technol., no. September, p. 25, 2017.

L. S. Yudhatama, I. Yansen, and E. Nokik, “A Way of Determining Accessory Pathway based on 12 Lead Electrocardiogram,” Arsip Kardiovask. Indones., vol. 4, no. 1, pp. 1–7, 2019.

M. A. Serhani, H. T. El Kassabi, H. Ismail, and A. N. Navaz, “ECG monitoring systems: Review, architecture, processes, and key challenges,” Sensors (Switzerland), vol. 20, no. 6, 2020, doi: 10.3390/s20061796.

J. Francis, “ECG monitoring leads and special leads,” Indian Pacing Electrophysiol. J., vol. 16, no. 3, pp. 92–95, 2016, doi: 10.1016/j.ipej.2016.07.003.

G. Valenza et al., “Predicting Mood Changes in Bipolar Disorder Through Heartbeat Nonlinear Dynamics,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 4, pp. 1034–1043, 2016, doi: 10.1109/JBHI.2016.2554546.

G. Petmezas et al., “Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets,” Biomed. Signal Process. Control, vol. 63, no. July 2020, 2021, doi: 10.1016/j.bspc.2020.102194.

Q. Wen et al., “Transverse cardiac slicing and optical imaging for analysis of transmural gradients in membrane potential and Ca2+ transients in murine heart,” J. Physiol., vol. 596, no. 17, pp. 3951–3965, 2018, doi: 10.1113/JP276239.

A. Agustiawan Surtono and G. A. Pauzi, “Sistem Instrumentasi Akuisisi Data EKG 12 Lead Berbasis Komputer,” J. Teor. dan Apl. Fis., vol. 04, no. 01, pp. 67–76, 2016.

V. Octaviani, A. Kurniawan, Y. Kusnendar Suprapto, and A. Zaini, “Alerting System for Sport Activity Based on ECG Signals using Proportional Integral Derivative,” Proceeding Electr. Eng. Comput. Sci. Informatics, vol. 4, no. 1, 2017, doi: 10.11591/eecsi.v4.1005.

D. Wicaksono, “Elektrokardiograf (Ekg) 12 Lead Tampil Pc (Bidang Frontal),” 2016.

B. I. Ulumiddiniyah, “Central Monitor PC Based Via Wireless (ECG and Heart Rate Parameters),” 2020.

T. H. Tsai and F. L. Tsai, “Efficient lossless compression scheme for multi-channel ECG signal processing,” Biomed. Signal Process. Control, vol. 59, p. 101879, 2020, doi: 10.1016/j.bspc.2020.101879.

S. Jayaraman, V. Sangareddi, R. Periyasamy, J. Joseph, and R. M. Shanmugam, “Modified limb lead ECG system effects on electrocardiographic wave amplitudes and frontal plane axis in sinus rhythm subjects,” Anatol. J. Cardiol., vol. 17, no. 1, pp. 46–54, 2017, doi: 10.14744/AnatolJCardiol.2016.6843.

A. M. G. Louzada, “Evolução do conceito de família -Juíza Ana Maria Gonçalves Louzada,” pp. 6–7, 2014.

P. Dedhia, H. Doshi, M. Rane, and G. Ahuja, “Low cost solar ECG with Bluetooth transmitter,” 2012 Int. Conf. Biomed. Eng. ICoBE 2012, no. February, pp. 419–423, 2012, doi: 10.1109/ICoBE.2012.6179050.

R. A. Rachman, I. D. G. H. Wisana, and P. C. Nugraha, “Development of a Low-Cost and Effisient ECG devices with IIR Digital Filter Design,” Indones. J. Electron. Electromed. Eng. Med. informatics, vol. 3, no. 1, pp. 21–28, 2021, doi: 10.35882/ijeeemi.v3i1.4.

Y. B. Rischa, V. Octantri, and H. G. Ariswati, “Electrocardiogram (ECG) 12 Lead PC Based With Storage (Transverse Plane),” vol. 1, no. 1, 2021.

R. D. Zuhroini, D. Titisari, and T. Hamzah, “Electrocardiograph with Two Display Measurements on a PC via Bluetooth Communication (Frontal),” vol. 1, no. 1, 2021.

H. Rifky, I. D. Gede, and P. C. Nugraha, “Remote ECG With 3 Measurement Display On Computer Via Bluetooth Communication (Limb Lead & Augmented Lead),” vol. 1, no. 1, 2021.

H. A. A, I. D. Gede, H. Wisana, and P. C. Nugraha, “REMOTE ECG WITH 3 MEASUREMENT DISPLAY ON PC VIA BLUETOOTH COMMUNICATION (Precordial Lead),” 2021.

A. N. Patel, Abhilasha M.; Gakare, Pankaj K.; Cheeran, “Real Time ECG Feature Extraction and Arrhythmia Detection on a Mobile Platform,” Int. J. Comput. Appl., vol. 44, no. 23, pp. 40–45, 2012, [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Real+Time+ECG+Feature+Extraction+and+Arrhythmia+Detection+on+a+Mobile+Platform#0.

M. W. Gifari, H. Zakaria, and R. Mengko, “Design of ECG Homecare:12-lead ECG acquisition using single channel ECG device developed on AD8232 analog front end,” Proc. - 5th Int. Conf. Electr. Eng. Informatics Bridg. Knowl. between Acad. Ind. Community, ICEEI 2015, pp. 371–376, 2015, doi: 10.1109/ICEEI.2015.7352529.

A. Das, “Advanced Portable ECG Simulator : Product Development & Validation,” pp. 187–191, 2019.

U. Gnaneshwara Chary and H. Kakarla, “Low Power Analog Multiplexers for ECG Applications,” J. Phys. Conf. Ser., vol. 1804, no. 1, 2021, doi: 10.1088/1742-6596/1804/1/012177.

G. D. Gargiulo, “True Unipolar ECG Machine for Wilson Central Terminal Measurements,” Biomed Res. Int., vol. 2015, 2015, doi: 10.1155/2015/586397.