II. Derece AV Blok Aritmik EKG Sinyallerinin VHDL ile FPGA-Tabanlı Tasarımı

Year 2022, , 1334 - 1345, 28.12.2022

Fatih Karataş , İsmail Koyuncu , Murat Alçın , Murat Tuna

https://doi.org/10.35414/akufemubid.1141837

Cited By: 1

Abstract

Biyomedikal uygulamaları son yılların önemli araştırma alanlarından biridir. Bu çalışma alanlarından birisi de biyomedikal sinyallerdir. Bu çalışmada, VHDL ile Xilinx-Vivado programı kullanılarak, yaşamsal belirti sinyallerine ait iki aritmik (II. Derece AV-blok tip-1 ve II. Derece AV-blok tip-2) EKG sinyali FPGA çipleri üzerinde çalışmak üzere tasarlanmış ve uygulanmıştır. Nümerik tabanlı EKG sinyalleri referans olarak alınmış ve FPGA tabanlı EKG sinyal tasarımından elde edilen sonuçlarla karşılaştırılmıştır. Daha sonra tasarımda kullanılan yapı ve çalışmadan elde edilen test sonuçları sunulmuştur. Tasarlanan EKG sinyalleri Zynq-7000 TC7Z020 FPGA için sentezlenmiştir ve 14 kanallı AN9767 DA modülü kullanılarak osiloskoptan gözlemlenmiştir. Place-Route işlemi sonrasında elde edilen FPGA çip kaynak tüketim değerleri sunulmuştur. Sonuçlara göre II. Derece AV-blok tip-1 sinyallerinin FPGA üzerinde en yüksek çalışma frekansı 651.827 MHz ve II. Derece AV-blok tip-2 sinyallerinin FPGA üzerinde en yüksek çalışma frekansı 663.504 MHz belirlenmiştir. FPGA tabanlı EKG sinyal tasarımından elde edilen maksimum MSE hata değerleri II. Derece AV AV-blok tip-1 sinyali için 2.0011E-03 ve II. Derece AV-blok tip-2 sinyali için 1.2754E-04’tür. Bu çalışmada, donanımsal olarak gerçeklenen FPGA tabanlı 2. derece AV blok aritmik EKG sinyalleri üretim sisteminin biyomedikal kalibrasyon uygulamalarında güvenle kullanılabileceği gösterilmiştir.

Keywords

Aritmi, Kalibrasyon, EKG, FPGA, Sinyal İşleme, VHDL

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Project Number

119E659

Thanks

Bu çalışma 119E659 numaralı proje ile Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmiştir.

Design of FPGA-based 2nd Degree AV Block Arrhythmic ECG Signals with VHDL

Abstract

Studies in the field of biomedicine are one of the substantial study areas that have recently taken place in the literature. Studies in these research areas are based on the processing of vital sign signals. This paper presents the design and implementation of two arrhythmic (2nd degree AV-block type-1 and 2nd degree AV-block type-2) ECG signs to be used in FPGA with Xilinx-Vivado software utilizing VHDL. Numeric ECG signs were taken as reference, then confront with values related to the design of FPGA based ECG sign. Design utilized in the implementation and test outcomes got from the work have been introduced. Implemented ECG signs have been synthesized for Zynq-7000 TC7Z020 FPGA and tracked using oscilloscope using 14-channel AN9767 DA module. After Place&Route, FPGA chip statistics were introduced. The maximum working frequencies of 2nd degree AV Block Type-1 signs and 2nd degree AV Block Type-2 signs in FPGA have been obtained as 651.827 MHz and 663.504 MHz, respectively. Maximum MSE rates from the FPGA-based ECG sign implementation for 2nd degree AV Block Type-1 and 2nd degree AV Block Type-2 signs have been obtained as 2.0011E-03 and 1.2754E-04, respectively. This paper demonstrates that the hardware designed FPGA-based ECG sign production system can be implemented on FPGA and can be utilized snugly in biomedical calibration applications. This paper demonstrates that FPGA-based ECG signal generation system, which is implemented as hardware, can be designed using FPGA chips and can be safely used in biomedicine areas.

Keywords

Arrhythmia, Calibration, ECG, FPGA, Signal Processing, VHDL

Project Number

119E659

References

• Agrawal, A., and Gawali, D. H., 2018. FPGA-based peak detection of ECG signal using histogram approach. In International Conference on Recent Innovations in Signal Processing and Embedded Systems, Bhopal India, 463–468.
• Akçay, M. Ş., Koyuncu, I., Alçın, M., and Tuna, M., 2020. Implementation of IQ-Math Based RadBas Activation Function on FPGA. In International Asian Congress on Contemporary Scıences-IV, Baku Azerbaijan, 599–607.
• Akgul, A., Calgan, H., Koyuncu, I., Pehlivan, I., and Istanbullu, A., 2015. Chaos-based engineering applications with a 3D chaotic system without equilibrium points. Nonlinear Dynamics, 84(2), 481–495.
• Alçın, M., Pehlivan, İ., and Koyuncu, İ., 2016. Hardware design and implementation of a novel ANN-based chaotic generator in FPGA. Optik-International Journal for Light and Electron Optics, 127(13), 5500–5005.
• Alçın, M., Tuna, M., Erdogmuş, P., and Koyuncu, İ., 2021. FPGA-based Dual Core TRNG Design Using Ring and Runge-Kutta-Butcher based on Chaotic Oscillator. Chaos Theory and Applications, 3(1), 20–28.
• Alemzadeh-Ansari, M. J., 2017. Chapter 3- Electrocardiography, In Practical Cardiology, Elsevier, 17–60.
• Alhelal, D., and Faezipour, M., 2017. Denoising and beat detection of ECG signal by Using FPGA. International Journal of High Speed Electronics and Systems, 26(3), 1740016.
• Arshad, Shaukat, S., Ali, A., Eleyan, A., Shah, A. S., and Ahmad, J., 2020. Chaos Theory and its Application: An Essential Framework for Image Encryption. Chaos Theory and Applications, 2(1), 17–22.
• Caner, C., Engin, M., and Engin, E. Z., 2008. The programmable ECG simulator. Journal of Medical Systems, 32(4), 355–359.
• Chien, J. R. C., 2007. Design of a programmable electrocardiogram generator using a microcontroller and the CPLD technology. In IECON 2007- 33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei Taiwan, 2152–2157.
• Cho, S., Lee, Y., and Chang, I., 2016. Designing a Novel ECG Simulator: Multi-Modality Electrocardiography into a Three- Dimensional Wire Cube Network. IEEE Technology and Society Magazine, 35(1), 75–84.
• Chowdhury, S. R., Chakrabarti, D., and Saha, H., 2008. FPGA realization of a smart processing system for clinical diagnostic applications using pipelined datapath architectures. Microprocessors and Microsystems, 32(2), 107–120.
• Desai, V., 2012. Electrocardiogram (ECG/EKG) using FPGA. Master’s Theses, The Faculty of the Department of Computer Science, San Jose State University, 45.
• Egila, M. G., El-Moursy, M. A., El-Hennawy, A. E., El-Simary, H. A., and Zaki, A., 2016. FPGA-based electrocardiography (ECG) signal analysis system using least-square linear phase finite impulse response (FIR) filter. Journal of Electrical Systems and Information Technology, 3(3), 513–526.
• Fu, H., Osborne, W., Clapp, R. G., Mencer, O., and Luk, W., 2009. Accelerating seismic computations using customized number representations on FPGAs. Eurasip Journal on Embedded Systems, 2009(1), 1–13.
• Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C. K., and Stanley, H. E., 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), 215-220.
• John, A. D., and Fleisher, L. A., 2006. Electrocardiography: The ECG. Anesthesiology Clinics of North America, 24(4), 697–715.
• Karataş, F., Koyuncu, İ., Tuna, M., and Alçın, M., 2020. Bulanık Mantık Üyelik Fonksiyonlarının Fpga Üzerinde Gerçeklenmesi. Bilgisayar Bilimleri ve Teknolojileri Dergisi, 1(1), 01-09.
• Karatas, F., Koyuncu, I., Alçın, M., and Tuna, M., 2020. Design of FPGA-based ECG Signal Using VHDL. 1st International Hazar Scientific Research Congress, Baku, Azerbaijan, 114–127.
• Karatas, F., Koyuncu, I., Tuna, M., Alçın, M., Avcioglu, E., and Akgul, A., 2022. Design and implementation of arrhythmic ECG signals for biomedical engineering applications on FPGA. The European Physical Journal Special Topics, 231, 869–884.
• Karataş, F., 2021. VHDL ile FPGA-tabanlı EKG simülatörü tasarımı. Yüksek Lisans Tezi, Afyon Kocatepe Üniversitesi Fen Bilimleri Enstitüsü, Afyonkarahisar, 145.
• Karataş, F., Koyuncu, İ., Alçın, M., and Tuna, M., 2021. Design and implementation of FPGA-based arrhythmic ECG signals using VHDL for biomedical calibration applications. International Advanced Researches and Engineering Journal, 5(3), 362–371.
• Koyuncu, I., Ozcerit, A. T., Pehlivan, I., and Avaroglu, E., 2014. Design and implementation of chaos based true random number generator on FPGA. 22nd Signal Processing and Communications Applications Conference, IEEE Computer Society, Trabzon, Turkey, 236–239.
• Koyuncu, I., Cetin, O., Katircioglu, F., and Tuna, M., 2015. Edge dedection application with FPGA based Sobel operatör. 23nd Signal Processing and Communications Applications Conference, IEEE, 1829–1832.
• Koyuncu, I., Akçay, M. S., Tuna, M., and Alcin, M., 2019. Implementation of IQ-Math-based Linear Activation Functions on FPGA. 1st International Congress of Multidisciplinary Studies and Research, Şanlıurfa, Türkiye, 114–124.
• Kumar, S., Singh, G., and Kaur, M., 2016. FPGA Implementation of Electrocardiography (ECG) Signal Processing 1. An International Journal of Engineering Sciences, 21(8), 58–70.
• Madiraju, N. S., Kurella, N., and Valapudasu, R., 2018. FPGA Implementation of ECG feature extraction using Time domain analysis. Electrical Engineering and Systems Science, Signal Processing (eess.SP); Hardware Architecture (cs.AR), 1–4.
• Meyer-Base, U., 2007. Introduction, In Digital Signal Processing with Field Programmable Gate Arrays, Springer, Berlin, Heidelberg. 1–52.
• Moysis, L., Tutueva, A., Volos, C., and Butusov, D., 2020. A Chaos Based Pseudo-Random Bit Generator Using Multiple Digits Comparison. Chaos Theory and Applications, 2(2), 58–68.
• Pan, J., Luan, F., Gao, Y., and Wei, Y., 2020. FPGA-Based Implementation of Stochastic Configuration Network for Robotic Grasping Recognition. IEEE Access, 8, 139966–139973.
• Paul, A. D., Urzoshi, K. R., Datta, R. S., Arsalan, A., and Azad, A. M., 2011. Design and development of microcontroller based ECG simulator. IFMBE Proceedings, 35, 292–295.
• Popa, R., 2019. ECG Signal Filtering in FPGA. 6th International Symposium on Electrical and Electronics Engineering, Galati, Romania, 1-6.
• Sezdi, M., 2012. Accreditation of Biomedical Calibration Measurements in Turkey. In Practical Concepts of Quality Control, IntechOpen, 79–99.
• Shirzadfar, H., and Khanahmadi, M., 2018. Design and Development of ECG Simulator and Microcontroller Based Displayer. Journal of Biosensors & Bioelectronics, 9(3), 1–9.
• Su, W., Liang, Y., Li, M., and Li, Y., 2019. The research and FPGA implementation of ECG signal preprocessing. International Conference on Biomedical and Health Informatics, IFMBE Proceedings, Springer Verlag, 167–168.
• Taşdemir, M. F., Koyuncu, I., Coşgun, E., and Katırcıoglu, F., 2020. Real-Time Fast Corner Detection Algorithm Based Image Processing Application on FPGA. International Asian Congress on Contemporary Sciences-III, IKSAD Publishing, Konya, Türkiye, 1–6.
• Tlelo-Cuautle, E., Rangel-Magdaleno, J., de la Fraga, L. G., Tlelo-Cuautle, E., Rangel-Magdaleno, J. de J., and De la Fraga, L. G., 2016. Introduction to Field-Programmable Gate Arrays, In Engineering Applications of FPGAs. Springer International Publishing, 1–32.
• Tuna, M., 2020. A novel secure chaos-based pseudo random number generator based on ANN-based chaotic and ring oscillator: design and its FPGA implementation. Analog Integrated Circuits and Signal Processing, 105(2), 167–181.
• Tuna, M., and Fidan, C. B., 2016. Electronic circuit design, implementation and FPGA-based realization of a new 3D chaotic system with single equilibrium point. Optik, 127(24), 11786–11799.
• Tuna, M., and Fidan, C. B., 2018. A Study on the importance of chaotic oscillators based on FPGA for true random number generating (TRNG) and chaotic systems. Journal of the Faculty of Engineering and Architecture of Gazi University, 33(2), 469–486.
• Tuncer, T., Avaroglu, E., Türk, M., and Ozer, A. B., 2015. Implementation of Non-periodic Sampling True Random Number Generator on FPGA. Informacije MIDEM, 44(4), 296–302.
• Do Vale Madeiro, J. P., Cortez, P. C., Salinet, J. L., Pedrosa, R. C., da Silva Monteiro Filho, J. M., and Brayner, A. R. A., 2018. Classical and modern features for interpretation of ECG signal. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition, Elsevier, 1–28.
• Wagner, G., 2005. Basic Electrocardiography, In Electrophysiological Disorders of the Heart, Elsevier Inc., 95–128.
• Yang, S., Lam, B., and Ng, C. M. N., 2018. Calibration of Electrocardiograph (ECG) Simulators. NCSLI Measure, 12(1), 46–53.
• Zhang, J. A., 2011. The design of ECG signal generator using PIC24F. Procedia Engineering, 24, 523–527.
• 1-https://www.xilinx.com/products/boards-and-kits/1-t9ddos.html, (01.02.2022)
• 2- https://litfl.com/ecg-library/, (05.02.2022)
• 3-https://www.skillstat.com/tools/ecg-simulator/, (10.02.2022)