Design and implementation of FPGA-based arrhythmic ECG signals using VHDL for biomedical calibration applications
Abstract
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Project Number
References
- 1. 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., Classical and Modern Features for Interpretation of ECG Signal., Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition, Elsevier, 2019. p. 1–28.
- 2. Yang, S., Lam, B., and Ng, C.M.N., Calibration of Electrocardiograph (ECG) Simulators. NCSLI Measure, 2018. 12(1): p. 46–53.
- 3. Koyuncu, İ., Özcerit, A.T., Pehlivan, İ., and Avaroğlu, E., Design and implementation of chaos based true random number generator on FPGA. In 2014 22nd IEEE Signal Processing and Communications Applications Conference, 2014. p. 236–239.
- 4. Meyer-Base, U., Introduction., Digital Signal Processing with Field Programmable Gate Arrays, Berlin, Heidelberg, 2014. p. 1–52.
- 5. Life in the fast lane. ECG Library Basics. [cited 2021 11 February]; Available from: https://litfl.com/ecg-library/.
- 6. PhysioNet. The research resource for complex physiologic signals. Physio Bank ATM, MIT, and BIH Arrhythmia Database. [cited 2021 11 February]; Available from: https://archive.physionet.org/.
- 7. Kumar, S., Singh, G., and Kaur, M., FPGA Implementation of Electrocardiography (ECG) Signal Processing 1. An International Journal of Engineering Sciences, 2016. p. 2229–6913.
- 8. Desai, V., Electrocardiogram (ECG/EKG) using FPGA, San Jose State University, Computer Science, Master's Thesis, USA, 2012. p. 11-15.
Details
Primary Language
English
Subjects
Software Engineering, Electrical Engineering
Journal Section
Research Article
Authors
Fatih Karataş
*
0000-0003-1877-5552
Türkiye
İsmail Koyuncu
0000-0003-4725-4879
Türkiye
Murat Alçın
0000-0002-2874-7048
Türkiye
Murat Tuna
0000-0003-3511-1336
Türkiye
Publication Date
December 15, 2021
Submission Date
April 16, 2021
Acceptance Date
July 8, 2021
Published in Issue
Year 2021 Volume: 5 Number: 3
Cited By
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Kırklareli Üniversitesi Mühendislik ve Fen Bilimleri Dergisi
https://doi.org/10.34186/klujes.1330804FPGA-enabled advanced deep learning accelerator for multi-class ECG signal classification
Analog Integrated Circuits and Signal Processing
https://doi.org/10.1007/s10470-026-02560-y
