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İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör

Year 2024, Volume: 39 Issue: 3, 1797 - 1814, 20.05.2024

Volkan Onursoy Recai Kılıç

https://doi.org/10.17341/gazimmfd.1167193

Abstract

Biyosinyaller insan vücudu tarafından üretilen elektriksel sinyallerdir. Bu sinyallerden ECG sinyali kalp kasları tarafından, EEG sinyali beyin tarafından, EMG sinyali ise vücuttaki çizgili kaslar tarafından üretilmektedir. Bu biyosinyaller ve test sinyalleri tıbbi cihazların kalibrasyon işlemlerinde ve İMA (insan-makine arayüzü) uygulamalarında kullanılmaktadır. Bu çalışma ile hem tıbbi cihazların kalibrasyon işleminde kullanılan test sinyallerini üreten hem de İMA uygulamalarındaki süreçlerde kullanılmak üzere sentetik ECG, EEG ve EMG sinyallerini üreten çok kanallı biyosinyal emülatörü geliştirilmiştir. Geliştirilen biyosinyal emülatörü ile biyosinyal üretimleri ve test sinyali üretimleri için gerekli olan farklı devre topolojileri anahtarlamalı kapasitör teknolojisine sahip FPAA ile ortadan kaldırılarak yeni bir yaklaşım önerilmiştir. Böylece tek bir hibrid devre topolojosi ve azaltılmış eleman sayısı sağlanarak biyosinyallerinin yanısıra test sinyalleri de fiziksel olarak Volt ve mV mertebesinde üretilmiştir. Bu sinyaller emülatör üzerinde sağlanan toplamda 14 adet çıkış ile gözlemlenmiştir. Ayrıca geliştirilen LabVIEW tabanlı biyosinyal simülatörü ile ayarlanabilir sinyal karakteristikleri sayesinde biyosinyalleri üretme ve kaydetme esnekliği getirilerek veritabanlarının getirdiği sınırlılığın ortadan kaldırılması hedeflenmiştir. Bununla birlikte simülatör, üretilen biyosinyalleri seri haberleşme standartlarına uygun olarak dış dünyaya aktarabilme yeteneğine sahiptir. Simülatörün emülatör ile haberleşmesi sağlanarak simülatörün veri aktarabilme yeteneği doğrulanmıştır.

Keywords

Biyosinyal sentetik ECG sentetik EEG sentetik EMG FPAA

References

  • Cannan, J., Hu, H., Human-Machine Interaction (HMI): A Survey Technical Report: CES-508; School of Computer Science & Electronic Engineering University of Essex, 1–16, 2011.
  • Kaur, A., Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review, J. Med. Eng. Technol., 45(1), 61-74, 2021.
  • Esposito, D., Centracchio, J., Andreozzi, E., Gargiulo, G. D., Naik, G. R., Bifulco, P., Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey, Sensors, 21(20), 6863, 2021.
  • Singh, H. P., Kumar, P., Developments in the human machine interface technologies and their applications: a review, J. Med. Eng. Technol., 45(7), 552-573, 2021.
  • Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T. P., Lin, C. T., EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications, IEEE/ACM Trans. Comput. Biol. Bioinf., 18(5), 1645-1666, 2021.
  • Khosla, A., Khandnor, P., Chand, T., A comparative analysis of signal processing and classification methods for different applications based on EEG signals, Biocybern. Biomed. Eng., 40(2), 649-690, 2020.
  • Wasimuddin, M., Elleithy, K., Abuzneid, A. S., Faezipour, M., Abuzaghleh, O., Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey, IEEE Access, 8, 177782-177803, 2020.
  • Rodríguez-Tapia, B., Soto, I., Martínez, D. M., Arballo, N. C., Myoelectric interfaces and related applications: Current state of EMG signal processing–A systematic review, IEEE Access, 8, 7792-7805, 2020.
  • Wijaya, N. H., Rijali, W. A., Shahu, N., Ahmad, I., Atmoko, R. A., The Design of Electro Cardiograph Signal Generator Using IC 14521 and IC 14017, Journal of Robotics and Control (JRC), 2(4), 270-273, 2021.
  • Ardila, S. O., Yulianto, E., Sumber, S., Digital ECG Phantom Design to Represent the Human Heart Signal for Early Test on ECG Machine in Hospital, International Journal of Advanced Health Science and Technology, 1(1), 14-19, 2021.
  • Wang, B., Chen, G., Rong, L., Yu, A., Wen, T., Zhang, Y., Hu, B., ECG diagnosis device based on machine learning, IEEE ICESIT, Chongqing-Çin, 383-386, 22-24 Kasım, 2021.
  • Gil, J. C. V., Gonzalez-Vargas, A. M., UAOSIM-ECG: An open-source 12-lead electrocardiography simulator, IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), Bogota-Kolombiya, 1-4, 13-15 Ekim, 2021.
  • Utomo, B., Wisana, I. D. G. H., Hamzah, T., Lamidi, L., Wicaksono, D. K., Baighout, S. A., ECG Simulator Based on Microcontroller Equipped with Arrhythmia Signal, Jurnal Teknokes, 15(2), 103-109, 2022.
  • Karataş, F., Koyuncu, İ., Tuna, M., Alçın, M., Avcioglu, E., Akgul, A., Design and implementation of arrhythmic ECG signals for biomedical engineering applications on FPGA, The European Physical Journal Special Topics, 231(5), 869-884, 2022.
  • Karakulak, E., ARM MCU-Based Experimental EEG Signal Generator Using Internal DAC and PWM Outputs, Gazi University Journal of Science, 35(3), 886-894, 2022.
  • Powell, M. P., Anso, J., Gilron, R., Provenza, N. R., Allawala, A. B., Sliva, D. D., Borton, D. A., NeuroDAC: an open-source arbitrary biosignal waveform generator, J. Neural Eng., 18(1), 016010, 2021.
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  • Apriadi, W., Gani, H. S., Prayitno, P., Ibrahim, N., Wijaya, S. K., Development of multithread acquisition system for high quality EEG signal measurement, In Journal of Physics: Conference Series, 1816(1), 012072, 2021.
  • Toresano, L. O. H. Z., Wijaya, S. K., Prawito, Sudarmaji, A., Badri, C., Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299, AIP Conference Proceedings, 1862(1), 030149, 2017.
  • Hendarwin, H., Prajitno, P., Wijaya, S. K., EEG data acquisition system 32 channels with relative power ratio based on Raspberry Pi 3, AIP Conference Proceedings, 2168(1), 020017, 2019.
  • Arif, R., Wijaya, S. K., Prajitno, P., Gani, H. S., Development of electroencephalography (EEG) data acquisition system based on FPGA PYNQ, AIP Conference Proceedings, 2092(1), 020026, 2019.
  • Apriadi, W., Wijaya, S. K., Development of electroencephalogram (EEG) based on ADS1 299EEGFE-PDK and LaunchPad MSP432P401R, 5th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME), Bandung-Endonezya, 241-245, 6-7 Kasım, 2019.
  • Bronzino, J. D., Medical devices and systems, CRC press, A.B.D., 2006.
  • Hasler, J., The Rise of SoC FPAA Devices, IEEE Custom Integrated Circuits Conference (CICC), Newport Beach-ABD, 1-8, 24-27 Nisan, 2022.
  • Zhu, Q., Li, H., Fu, Y., Wang, C. X., Tan, Y., Chen, X., Wu, Q., A novel 3D non-stationary wireless MIMO channel simulator and hardware emulator, IEEE Trans. Commun., 66(9), 3865-3878, 2018.
  • Ram, J. P., Manghani, H., Pillai, D. S., Babu, T. S., Miyatake, M., Rajasekar, N., Analysis on solar PV emulators: A review, Renewable Sustainable Energy Rev., 81, 149-160, 2018.

New approach in synthetic biosignal generation for human-machine interface applications: FPAA based emulator

Year 2024, Volume: 39 Issue: 3, 1797 - 1814, 20.05.2024

Volkan Onursoy Recai Kılıç

https://doi.org/10.17341/gazimmfd.1167193

Abstract

Biosignals are electrical signals produced by the human body. ECG signal is produced by the heart muscles, the EEG signal is produced by the brain, and the EMG signal is produced by the striated muscles in the body. These biosignals and test signals are used in calibration processes of medical devices and HMI (human-machine interface) applications. With this study, a multi-channel biosignal emulator that produces both test signals that can be used in calibration process of medical devices and synthetic ECG, EEG and EMG signals to be used in the processes of HMI applications has been developed. A new approach is proposed by eliminating the different circuit topologies required for biosignal generation and test signal generation using switched capacitor technology FPAA based emulator. Thus, by providing a single hybrid circuit topology and reduced component count, ECG, EEG and EMG biosignals as well as test signals are physically produced in the level of Volts and mV. These signals were observed with a total of 14 outputs provided on the emulator. In addition, with the LabVIEW-based biosignal simulator developed, it is aimed to eliminate the limitation of databases by bringing flexibility to generate and record biosignals thanks to adjustable signal characteristics. However, the simulator has the ability to transfer the produced biosignals to the outside world in accordance with serial communication standards. Transferring data ability of the simulator has been verified by ensuring that the simulator communicates with the emulator.

Keywords

Biosignal synthetic ECG synthetic EEG synthetic EMG FPAA

References

  • Cannan, J., Hu, H., Human-Machine Interaction (HMI): A Survey Technical Report: CES-508; School of Computer Science & Electronic Engineering University of Essex, 1–16, 2011.
  • Kaur, A., Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review, J. Med. Eng. Technol., 45(1), 61-74, 2021.
  • Esposito, D., Centracchio, J., Andreozzi, E., Gargiulo, G. D., Naik, G. R., Bifulco, P., Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey, Sensors, 21(20), 6863, 2021.
  • Singh, H. P., Kumar, P., Developments in the human machine interface technologies and their applications: a review, J. Med. Eng. Technol., 45(7), 552-573, 2021.
  • Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T. P., Lin, C. T., EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications, IEEE/ACM Trans. Comput. Biol. Bioinf., 18(5), 1645-1666, 2021.
  • Khosla, A., Khandnor, P., Chand, T., A comparative analysis of signal processing and classification methods for different applications based on EEG signals, Biocybern. Biomed. Eng., 40(2), 649-690, 2020.
  • Wasimuddin, M., Elleithy, K., Abuzneid, A. S., Faezipour, M., Abuzaghleh, O., Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey, IEEE Access, 8, 177782-177803, 2020.
  • Rodríguez-Tapia, B., Soto, I., Martínez, D. M., Arballo, N. C., Myoelectric interfaces and related applications: Current state of EMG signal processing–A systematic review, IEEE Access, 8, 7792-7805, 2020.
  • Wijaya, N. H., Rijali, W. A., Shahu, N., Ahmad, I., Atmoko, R. A., The Design of Electro Cardiograph Signal Generator Using IC 14521 and IC 14017, Journal of Robotics and Control (JRC), 2(4), 270-273, 2021.
  • Ardila, S. O., Yulianto, E., Sumber, S., Digital ECG Phantom Design to Represent the Human Heart Signal for Early Test on ECG Machine in Hospital, International Journal of Advanced Health Science and Technology, 1(1), 14-19, 2021.
  • Wang, B., Chen, G., Rong, L., Yu, A., Wen, T., Zhang, Y., Hu, B., ECG diagnosis device based on machine learning, IEEE ICESIT, Chongqing-Çin, 383-386, 22-24 Kasım, 2021.
  • Gil, J. C. V., Gonzalez-Vargas, A. M., UAOSIM-ECG: An open-source 12-lead electrocardiography simulator, IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), Bogota-Kolombiya, 1-4, 13-15 Ekim, 2021.
  • Utomo, B., Wisana, I. D. G. H., Hamzah, T., Lamidi, L., Wicaksono, D. K., Baighout, S. A., ECG Simulator Based on Microcontroller Equipped with Arrhythmia Signal, Jurnal Teknokes, 15(2), 103-109, 2022.
  • Karataş, F., Koyuncu, İ., Tuna, M., Alçın, M., Avcioglu, E., Akgul, A., Design and implementation of arrhythmic ECG signals for biomedical engineering applications on FPGA, The European Physical Journal Special Topics, 231(5), 869-884, 2022.
  • Karakulak, E., ARM MCU-Based Experimental EEG Signal Generator Using Internal DAC and PWM Outputs, Gazi University Journal of Science, 35(3), 886-894, 2022.
  • Powell, M. P., Anso, J., Gilron, R., Provenza, N. R., Allawala, A. B., Sliva, D. D., Borton, D. A., NeuroDAC: an open-source arbitrary biosignal waveform generator, J. Neural Eng., 18(1), 016010, 2021.
  • Netech Corporation. MiniSim 330 EEG Simulator. https://www.netechcorp.us/Products/details/330-EEG-Simulator_71. Erişim tarihi: Ağustos 19, 2022.
  • Netech Corporation. MiniSim 1000 ECG Simulator. https://www.netechcorp.us/Products/details/MiniSim-1000-Patient-Simulator_70. Erişim tarihi: Ağustos 19, 2022.
  • Rigel Medical. Uni-Sim. https://www.rigelmedical.com/gb/products/patient-simulation/patient-simulators/370a930-uni-sim. Erişim tarihi: Ağustos 19, 2022.
  • Rigel Medical. PatSim 200. https://www.rigelmedical.com/gb/products/patient-simulation/patient-simulators/404a920-patsim-200. Erişim tarihi: Ağustos 19, 2022.
  • Kotowski, K., Fabian, P., Stapor, K., Machine learning approach to automatic recognition of emotions based on bioelectrical brain activity, Simulations in Medicine, De Gruyter, 15-34, 2020.
  • Apriadi, W., Gani, H. S., Prayitno, P., Ibrahim, N., Wijaya, S. K., Development of multithread acquisition system for high quality EEG signal measurement, In Journal of Physics: Conference Series, 1816(1), 012072, 2021.
  • Toresano, L. O. H. Z., Wijaya, S. K., Prawito, Sudarmaji, A., Badri, C., Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299, AIP Conference Proceedings, 1862(1), 030149, 2017.
  • Hendarwin, H., Prajitno, P., Wijaya, S. K., EEG data acquisition system 32 channels with relative power ratio based on Raspberry Pi 3, AIP Conference Proceedings, 2168(1), 020017, 2019.
  • Arif, R., Wijaya, S. K., Prajitno, P., Gani, H. S., Development of electroencephalography (EEG) data acquisition system based on FPGA PYNQ, AIP Conference Proceedings, 2092(1), 020026, 2019.
  • Apriadi, W., Wijaya, S. K., Development of electroencephalogram (EEG) based on ADS1 299EEGFE-PDK and LaunchPad MSP432P401R, 5th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME), Bandung-Endonezya, 241-245, 6-7 Kasım, 2019.
  • Bronzino, J. D., Medical devices and systems, CRC press, A.B.D., 2006.
  • Hasler, J., The Rise of SoC FPAA Devices, IEEE Custom Integrated Circuits Conference (CICC), Newport Beach-ABD, 1-8, 24-27 Nisan, 2022.
  • Zhu, Q., Li, H., Fu, Y., Wang, C. X., Tan, Y., Chen, X., Wu, Q., A novel 3D non-stationary wireless MIMO channel simulator and hardware emulator, IEEE Trans. Commun., 66(9), 3865-3878, 2018.
  • Ram, J. P., Manghani, H., Pillai, D. S., Babu, T. S., Miyatake, M., Rajasekar, N., Analysis on solar PV emulators: A review, Renewable Sustainable Energy Rev., 81, 149-160, 2018.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Volkan Onursoy 0000-0001-7915-857X

Recai Kılıç 0000-0002-5069-6603

Early Pub Date May 16, 2024
Publication Date May 20, 2024
Submission Date August 26, 2022
Acceptance Date November 12, 2023
Published in Issue Year 2024 Volume: 39 Issue: 3

Cite

APA Onursoy, V., & Kılıç, R. (2024). İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(3), 1797-1814. https://doi.org/10.17341/gazimmfd.1167193
AMA Onursoy V, Kılıç R. İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör. GUMMFD. May 2024;39(3):1797-1814. doi:10.17341/gazimmfd.1167193
Chicago Onursoy, Volkan, and Recai Kılıç. “İnsan-Makine arayüz Uygulamaları için Sentetik Biyosinyal üretiminde Yeni yaklaşım: FPAA Tabanlı emülatör”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, no. 3 (May 2024): 1797-1814. https://doi.org/10.17341/gazimmfd.1167193.
EndNote Onursoy V, Kılıç R (May 1, 2024) İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 3 1797–1814.
IEEE V. Onursoy and R. Kılıç, “İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör”, GUMMFD, vol. 39, no. 3, pp. 1797–1814, 2024, doi: 10.17341/gazimmfd.1167193.
ISNAD Onursoy, Volkan - Kılıç, Recai. “İnsan-Makine arayüz Uygulamaları için Sentetik Biyosinyal üretiminde Yeni yaklaşım: FPAA Tabanlı emülatör”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/3 (May 2024), 1797-1814. https://doi.org/10.17341/gazimmfd.1167193.
JAMA Onursoy V, Kılıç R. İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör. GUMMFD. 2024;39:1797–1814.
MLA Onursoy, Volkan and Recai Kılıç. “İnsan-Makine arayüz Uygulamaları için Sentetik Biyosinyal üretiminde Yeni yaklaşım: FPAA Tabanlı emülatör”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 39, no. 3, 2024, pp. 1797-14, doi:10.17341/gazimmfd.1167193.
Vancouver Onursoy V, Kılıç R. İnsan-makine arayüz uygulamaları için sentetik biyosinyal üretiminde yeni yaklaşım: FPAA tabanlı emülatör. GUMMFD. 2024;39(3):1797-814.