Comparative SNR Analysis Between Instrument ADAS1000 and AD620

Authors

  • Tri Arief Sardjono Institut Teknologi Sepuluh Nopember
  • Hendra Kusuma Institut Teknologi Sepuluh Nopember
  • Tasripan Institut Teknologi Sepuluh Nopember
  • Kharis Sugiarto Institut teknologi kalimantan

DOI:

https://doi.org/10.37034/jsisfotek.v4i3.145

Keywords:

Comparative, AD620, ADAS1000, ECG 12 Channel, Noise

Abstract

The semiconductor technology for a certain function, such as instrumentation amplifier ICs is currently developing very fast, commercially available and can be obtained easily on the market. An instrumentation amplifier is a very important part in the process of data acquisition, especially for biopotential signals from the human body because its amplitude is very small and susceptible to noise interference. The selection of the proper amplifier instrumentation will produce an accurate biopotential signal reading. This paper explains the use of 2 types of instrumentation amplifiers, namely AD620 and ADAS1000, which are used in the design and realization of 12-channel ECG (Electrocardiograph). The performance and noise resistance of the two instrumentation amplifiers are compared and analyzed so that an appropriate instrumentation amplifier can be determined especially in the case of 12-channel ECG applications. 12-channel ECG was chosen because of the complexity of the design and can provide more detailed information as well as to detect the abnormalities heart’s functions. The results shows that 12-channel ECG using AD620 instrumentation amplifier has an SNR value below 12.04 dB, while using the ADAS1000 instrumentation amplifier has an SNR value below 35.5 dB and it is more resistant to noise interference.

References

Nawal, M., Sharma, M. K., & Bundele, M. M. (2016, December). Design and implementation of human identification through physical activity aware 12 lead ECG. In 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). IEEE. DOI: 10.1109/ICRAIE.2016.7939536

Zhu, X., Yoshida, K., Yamanobe, W., Yamamoto, Y., Chen, W., & Wei, D. (2003, October). Conversion of the ambulatory ECG to the standard 12-lead ECG: a preliminary study. In IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 2003. (pp. 48-49). IEEE. DOI: 10.1109/APBME.2003.1302577

Mengko, R., & Sutjiady, F. (2013, November). Design and implementation of 12 Lead ECG signals interpretation system. In 2013 3rd International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME) (pp. 278-282). IEEE. DOI: 10.1109/ICICI-BME.2013.6698508

Carey, M. G., Al-Zaiti, S. S., & Butler, R. A. (2010, September). Characteristics of the standard 12-lead Holter ECG in professional firefighters. In 2010 Computing in Cardiology (pp. 685-688). IEEE.

Kanakapriya, K., Mandali, A., & Manivannan, M. (2011, December). ECG simulation for Myocardial Infarction diagnosis in high fidelity mannequins. In 2011 Annual IEEE India Conference (pp. 1-5). IEEE. DOI: 10.1109/INDCON.2011.6139634

Ortigosa, N., Osca, J., Jiménez, R., Rodríguez, Y., Fernández, C., & Galbis, A. (2016, September). Predictive analysis of Cardiac Resynchronization Therapy response by means of the ECG. In 2016 Computing in Cardiology Conference (CinC) (pp. 753-756). IEEE.

Belgacem, N., Assous, S., & Bereksi-Reguig, F. (2011, May). Bluetooth portable device and Matlab-based GUI for ECG signal acquisition and analisys. In International Workshop on Systems, Signal Processing and their Applications, WOSSPA (pp. 87-90). IEEE. DOI: 10.1109/WOSSPA.2011.5931420

Purnama, S. I., Kusuma, H., & Sardjono, T. A. (2019, May). Electrocardiogram Feature Recognition Algorithm with Windowing and Adaptive Thresholding. In Journal of Physics: Conference Series (Vol. 1201, No. 1, p. 012048). IOP Publishing. doi:10.1088/1742-6596/1201/1/012048

Sobhani, S., Sara, R., Aghaee, A., Pirzadeh, P., Miandehi, E. E., Shafiei, S., ... & Eslami, S. (2022). Body mass index, lipid profile, and hypertension contribute to prolonged QRS complex. Clinical Nutrition ESPEN, 50, 231-237. DOI: https://doi.org/10.1016/j.clnesp.2022.05.011

Gacek, A., & Pedrycz, W. (Eds.). (2011). ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. Springer Science & Business Media. DOI: 10.1007/978-0-85729-868-3

Ryder, K. L., Phan, A. M., Campola, M. J., Khachatrian, A., McMorrow, D. P., & Ildefonso, A. (2022). Analog Devices AD620 Instrumentation Amplifier Laser Single-Event Effects Characterization Test Report.

Tretriluxana, S., Wungpila, P., Chitsakul, K., & Kato, K. (2016). A Design of Low Cost 12-Lead ECG Acquisition System Using Raspberry PI. Journal of Engineering and Digital Technology (JEDT), 4(1), 1-5.

Hadiyoso, S., Usman, K., Rizal, A., & Sigit, R. (2013). Microcontroller-based Mini Wearable ECG Design Desain Mini wearable ECG Berbasis Mikrokontroler. Pusat Penelitian Informatika-LIPI Jurnal INKOM Vol. 7 No. 2 Hal. 57-106 Bandung, p-ISSN 1979-8059 November 2013 e-ISSN 2302-6146, 99.

Devices, A. (2012). Low power, five electrode electrocardiogram (ECG) analog front end. ADAS1000 datasheet.

Triwiyanto, T., Wahyunggoro, O., Nugroho, H. A., & Herianto, H. (2017). Evaluating the performance of Kalman filter on elbow joint angle prediction based on electromyography. International Journal of Precision Engineering and Manufacturing, 18(12), 1739-1748. DOI: https://doi.org/10.1007/s12541-017-0202-5

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Published

25-08-2022

How to Cite

Sardjono, T. A. . ., Kusuma, H. ., Tasripan, & Sugiarto, K. (2022). Comparative SNR Analysis Between Instrument ADAS1000 and AD620. Jurnal Sistim Informasi Dan Teknologi, 4(3), 123–127. https://doi.org/10.37034/jsisfotek.v4i3.145

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