Improved Horizon Calculation and Performance Comparison of I-UFIR and Filtering Techniques for Baseline Wander Removal in ECG Signals
DOI:
https://doi.org/10.18196/jrc.v6i3.24778Keywords:
Baseline Wander, I-UFIR Filter, Horizon for BLW, Savitzky-Golay, WaveletAbstract
Removing Baseline Wander (BLW) is crucial in ECG signal filtering without altering its morphology, as BLW can hide critical diagnostic features such as ST-segment deviations, T-wave changes, and P-wave morphology. These distortions can lead to misinterpretations of the ECG, potentially resulting in incorrect diagnoses or missed clinical conditions, such as myocardial ischemia or arrhythmias. In this study, the term optimal is defined by the ability of methods to effectively remove baseline wander while preserving the key morphological features of the ECG signal, such as the ST-segment, T-wave, and P-wave, with minimal distortion. Some algorithms with minimal information and tuning offer acceptable BLW approximations. This study applies the I-UFIR filter to remove BLW and compares its performance with the Savitzky-Golay (S-G) filter and Wavelet transform with 9 and 10 decompositions. The Savitzky-Golay (S-G) filter was chosen for its effectiveness in smoothing baseline wander while maintaining the original morphology of the ECG signal. Wavelet transform was selected for its multi-resolution analysis, which enables the separation of BLW from essential ECG features. To assess the performance of these methods, Mean Square Error (MSE), Root Mean Square Error, and box plot comparisons were used to quantify and visually analyze their effectiveness in baseline correction. Since determining the horizon for I-UFIR and S-G filters is challenging, two approaches are compared, first a traditional calculation and one based on the signal's sampling frequency. The comparison of these approaches for computing the optimal horizon can simplify BLW estimation, reducing both computational effort and time, as the traditional approach depends on iterative calculations. Furthermore, two ECG signal sources are used for testing, one synthetic and the other real acquired using the ECG sensor AD8232, ADC ADS1115, and microcontroller MEGA 2560. The AD8232 ECG sensor records the electrical signal of the heart, the ADS1115 ADC converts the analog signal into a digital form for processing, and the MEGA 2560 microcontroller coordinates data acquisition, ensuring precise and dependable ECG signal capture for analysis. However, it is important to note that the study is based on ECG signals obtained from a single individual under specific conditions and recorded using a particular hardware setup, which may limit the generalizability of the findings to a broader population or different ECG recording systems and environments. Wavelet decompositions yield the best results for synthetic signals, while I-UFIR and S-G filters perform better with real signals.
References
World Health Organization (WHO), Noncommunicable diseases, World Health Organization (WHO), 2023,https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases.
M. Y. Henein, S. Vancheri, G. Longo, and F. Vancheri, “The role of inflammation in cardiovascular disease,” International Journal of Molecular Sciences, vol. 23, no. 21, p. 12906, 2022, doi: 10.3390/ijms232112906.
J. Ferlay, M. Colombet, I. Soerjomataram, D. M. Parkin, M. Piñeros, A. Znaor, and F. Bray, “Cancer statistics for the year 2020: An overview,” International Journal of Cancer, vol. 149, no. 4, pp. 778-789, 2021, doi: 10.1002/ijc.33588.
G. Viegi, S. Maio, S. Fasola, and S. Baldacci, “Global burden of chronic respiratory diseases,” Journal of Aerosol Medicine and Pulmonary Drug Delivery, vol. 33, no. 4, pp. 171-177, 2020, doi: 10.1089/jamp.2019.1576.
J. B. Cole, and J. C. Florez, “Genetics of diabetes mellitus and diabetes complications,” Nature Reviews Nephrology, vol. 16, no. 7, pp. 377-390, 2020, doi: 10.1038/s41581-020-0278-5.
H. T. Cheng, X. Xu, P. S. Lim, and K. Y. Hung, “Worldwide epidemiology of diabetes-related end-stage renal disease, 2000–2015”, Diabetes Care, vol. 44, no. 1, pp. 89-97, 2021, doi: 10.2337/dc20-1913.
World Health Organization (WHO), Cardiovascular diseases. World Health Organization (WHO), 2024, https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1.
G. Lippi and F. Sanchis-Gomar, “An estimation of the worldwide epidemiologic burden of physical inactivity-related ischemic heart disease,” Cardiovascular Drugs and Therapy, vol. 34, pp. 133-137, 2020, doi: 10.1007/s10557-019-06926-5.
L. B. Goldstein, “Introduction for focused updates in cerebrovascular disease,” Stroke, vol. 51, no. 3, pp. 708-710, 2020, doi: 10.1161/STROKEAHA.119.024159.
S. H. Ghamari et al., “Rheumatic heart disease is a neglected disease relative to its burden worldwide: findings from global burden of disease 2019,” Journal of the American Heart Association, vol. 11, no. 13, p. e025284, 2022, doi: 10.1161/JAHA.122.025284.
C. Lai, S. Zhou, and N. A. Trayanova, “Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification,” Philosophical Transactions of the Royal society A, vol. 379, no. 2212, p. 20200258, 2021, doi: 10.1098/rsta.2020.0258.
H. Li and P. Boulanger, “A survey of heart anomaly detection using ambulatory electrocardiogram (ECG),” Sensors, vol. 20, no. 5, p. 1461, 2020, doi:10.3390/s20051461.
K. Nezamabadi, N. Sardaripour, B. Haghi, and M. Forouzanfar, “Unsupervised ECG analysis: A review”, IEEE Reviews in Biomedical Engineering, vol. 16, pp. 208-224, 2022, doi: 10.1109/RBME.2022.3154893.
H. De Carvalho et al., “Electrocardiographic abnormalities in COVID-19 patients visiting the emergency department: a multicenter retrospective study,” BMC Emergency Medicine, vol. 21, pp. 1-7, 2021, doi: 10.1186/s12873-021-00539-8.
A. Pal, R. Srivastva, and Y. N. Singh, “CardioNet: An efficient ECG arrhythmia classification system using transfer learning,” Big Data Research, vol. 26, p. 100271, 2021, doi: 10.1016/j.bdr.2021.100271.
P. Madona, R. I. Basti, and M. M. Zain, “PQRST wave detection on ECG signals,” Gaceta sanitaria, vol. 35, pp. S364-S369, 2021, doi: 10.1016/j.gaceta.2021.10.052.
S. Chatterjee, R. S. Thakur, R. N. Yadav, L. Gupta, and D. K. Raghuvanshi, “Review of noise removal techniques in ECG signals,” IET Signal Processing, vol. 14, no. 9, pp. 569-590, 2020, doi: 10.1049/iet-spr.2020.0104.
H. Y. Mir and O. Singh, “ECG denoising and feature extraction techniques–a review,” Journal of medical engineering & technology, vol. 45, no. 8, pp. 672-684, 2021, doi: 10.1080/03091902.2021.1955032.
J. Zhang, Y. Guo, X. Dong, T. Wang, J. Wang, X. Ma, and H. Wang, “Opportunities and challenges of noise interference suppression algorithms for dynamic ECG signals in wearable devices: A review,” Measurement, vol. 259, p. 117067, 2025, doi: 10.1016/j.measurement.2025.117067.
M. R. Alla, and C. Nayak, “A robust ECG signal enhancement technique through optimally designed adaptive filters,” Biomedical Signal Processing and Control, vol. 95, p. 106434, 2024, doi: 10.1016/j.bspc.2024.106434.
A. H. Kashou et al., “ECG interpretation proficiency of healthcare professionals,” Current Problems in Cardiology, vol. 48, no. 10, 2023, doi: 10.1016/j.cpcardiol.2023.101924.
J. Frampton, A. R. Ortengren, and E. P. Zeitler, “Arrhythmias after acute myocardial infarction,” The Yale journal of biology and medicine, vol. 96, no. 1, p. 83, 2023, doi: 10.59249/LSWK8578.
X. Xu, Z. Wang, J. Yang, X. Fan, and Y. Yang, “Burden of cardiac arrhythmias in patients with acute myocardial infarction and their impact on hospitalization outcomes: insights from China acute myocardial infarction (CAMI) registry,” BMC Cardiovascular Disorders, vol. 24, no. 1, p. 218, 2024, doi: 10.1186/s12872-024-03889-w.
A. Menditto, L. Mancinelli, and R. Antonicelli, “Autonomic nervous system and cardiac arrhythmias,” In Autonomic Disorders in Clinical Practice, pp. 43-64, 2023, doi: 10.1007/978-3-031-43036-7_4.
T. Neycheva, D. Dobrev, and V. Krasteva, “Common-mode driven synchronous filtering of the powerline interference in ECG,” Applied Sciences, vol. 12, no. 22, pp. 1-29, 2022, doi: 10.3390/app122211328.
J. Shen, X. Li, Y. Wang, Y. Li, J. Bian, X. Zhu, X. He, and J. Li, “Anti-motion Interference Electrocardiograph Monitoring System-A Review,” IEEE Sensors Journal, vol. 24, no. 10, pp. 15727-15747, 2024, doi: 10.1109/JSEN.2024.3383872.
A. Singhal, P. Singh, B. Fatimah, and R. B. Pachori, “An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique,” Biomedical Signal Processing and Control, vol. 57, p. 101741, 2020, doi: 10.1016/j.bspc.2019.101741.
S. Boda, M. Mahadevappa, and P. K. Dutta, “A hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT,” Biomedical Signal Processing and Control, vol. 67, p. 102466, 2021, doi: 10.1016/j.bspc.2021.102466.
X. Wan, H. Wu, F. Qiao, F. Li, Y. Li, Y. Yan, and J. Wei, “Electrocardiogram baseline wander suppression based on the combination of morphological and wavelet transformation-based filtering”, Computational and Mathematical Methods in Medicine, vol. 2019, pp. 1-7, 2019, doi: 10.1155/2019/7196156.
M. Khalili, H. GholamHosseini, A. Lowe, and M. M. Y. Kuo, “Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques,” Medical & Biological Engineering & Computing, vol. 62, pp. 3599-3622, 2024, doi: 10.1007/s11517-024-03165-1.
M. K. Islam, A. Rastegarnia, and S. Sanei, “Signal artifacts and techniques for artifacts and noise removal,” Signal Processing Techniques for Computational Health Informatics, pp. 23-79, 2021, doi:10.1007/978-3-030-54932-9_2.
L. Littmann, “Electrocardiographic artifact,” Journal of Electrocardiology, vol. 64, pp. 23-29, 2021, doi: 10.1016/j.jelectrocard.2020.11.006.
S. Ozaydin and I. Ahmad, “Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal,” Fluctuation and Noise Letters, vol. 23, no. 4, 2024, doi: 10.1142/S0219477524500469.
F. P. Romero, D. C. Piñol, and C. R. V. Seisdedos, “DeepFilter: An ECG baseline wander removal filter using deep learning techniques,” Biomedical Signal Processing and Control, vol. 70, pp. 1-122, 2021, doi: 10.48550/arXiv.2101.03423.
H. Li, G. Ditzler, J. Roveda, and A. Li, “Descod-ecg: Deep score-based diffusion model for ecg baseline wander and noise removal,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 9, pp. 5081-5091, 2023, doi: 10.1109/JBHI.2023.3237712.
L. Yao and Z. Pan, “A new method based CEEMDAN for removal of baseline wander and powerline interference in ECG signals,” Optik, vol. 223, p. 165566, 2020, doi: 10.1016/j.ijleo.2020.165566.
R. Kher, “Signal Processing Techniques for Removing Noise from ECG Signals”, Journal of Biomedical Engineering and Research, vol. 3, pp. 1-9, 2019, doi: 10.17303/jber.2019.3.101.
A. Pashko, I. Krak, O. Stelia, and W. Wojcik, “Baseline wander correction of the electrocardiogram signals for effective preprocessing,” Lecture Notes in Computational Intelligence and Communications Technologies, vol. 77, pp. 507-518, 2022, doi: 10.1007/978-3-030-82014-5_34.
J. Rahul, and M. Sora, “An efficient algorithm for the removal of motion artifacts in wearable ECG technology,” Iran J Comput Sci., vol. 8, pp. 69–78, 2025. doi: 10.1007/s42044-024-00208-6.
A. A. Fedotov, “An adaptive method for correction of the ECG signal baseline drift using multiresolution wavelet transforms,” Biomedical Engineering, vol. 55, no. 6, pp. 420-424, 2022, doi: 10.1007/s10527-022-10149-8.
H. B. Hwang, H. Kwon, B. Chung, J. Lee, and I. Y. Kim, “ECG authentication based on non-linear normalization under various physiological conditions,” Sensors, vol. 21, no. 21, p. 6966, 2021, doi: 10.3390/s21216966.
F. P. Romero, L. V. Romaguera, C. F. F. Costa-Filho, M. G. Fernandes, J. Evangelista, and C. R. V. Seisdedos, “Baseline wander removal methods for ECG signals: A comparative study,” Revista Cubana de Ciencias Informáticas, vol. 14, no. 1, pp. 1-19, 2019, doi: 10.48550/arXiv.1807.11359.
N. E. Menaceur, S. Kouah, and M. Derdour, “Adaptive Filtering Strategies for ECG Signal Enhancement: A Comparative Study,” 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS), pp. 1-6, 2024 doi: 10.1109/PAIS62114.2024.10541144.
M. R. Lakehal, and Y. Ferdi, “Baseline wander and power line interference removal from physiological signals using fractional notch filter optimized through genetic algorithm,” Arabian Journal for Science and Engineering, vol. 49, no. 12, pp. 16647-16667, 2024, doi: 10.1007/s13369-024-09145-9.
P. Madan, V. Singh, D. P. Singh, M. Diwakar, and A. Kishor, “Denoising of ECG signals using weighted stationary wavelet total variation,” Biomedical Signal Processing and Control, vol. 73, p. 103478, 2022, doi: 10.1016/j.bspc.2021.103478.
G. B. Moody and R. G. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, 2001, doi: 10.1109/51.932724.
A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, vol. 101, no. 23, pp. 215-220, 2000, doi: 10.1161/01.CIR.101.23.e215.
M. J. S. M. A. Maraikkayar et al., “Novel digital filter design for noise removal in fetal ECG signals,” The Open Biomedical Engineering Journal, vol. 17, no.1, pp. 1-8, 2023, doi: 10.2174/18741207-v16-e221227-2022-HT27-3589-8.
M. Aqil, A. Jbari, and A. Bourouhou, “Comparison of ECG baseline wander removal techniques and improvement based on moving average of wavelet approximation coefficients,” International Journal Bioautomation, vol. 25, no. 2, pp. 183-204, 2021, doi: 10.7546/ijba.2021.25.2.000770.
R. M. Raj, V. Rajesh, S. Saravanan, M. S. P. Balaji, and V. Elamaran, “Baseline wandering noise removal using high-speed IIR filters with an FPGA implementation,” Microelectronic Devices, Circuits and Systems: Second International Conference, ICMDCS 2021, vol. 1392, pp. 55-65, 2021, doi: 10.1007/978-981-16-5048-2_5.
R. Chitra and E. Priya, “Digital filter implementation for removal of baseline wander in ECG signals,” In International Conference on Automation, Signal Processing, Instrumentation and Control, Singapore: Springer Nature Singapore, pp. 2711-2718, 2020, doi: 10.1007/978-981-15-8221-9_254.
I. Houamed, L. Saidi, and F. Srairi, “ECG signal denoising by fractional wavelet transform thresholding,” Research on Biomedical Engineering, vol. 36, pp. 349-360, 2020, doi: 10.1007/s42600-020-00075-7.
C. C. Chen, and F. R. Tsui, “Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends,” BMC medical informatics and decision making, vol. 20, pp. 1-10, 2020, doi: 10.1186/s12911-020-01349-x.
S. A. Malik, S. A. Parah, and B. A. Malik, “Power line noise and baseline wander removal from ECG signals using empirical mode decomposition and lifting wavelet transform technique,” Health and Technology, vol. 12, no. 4, pp. 745-756, 2022, doi:10.1007/s12553-022-00662-x.
T. Tearwattanarattikal and A. Lek-uthai, “Comparison of baseline wander correction methods for handheld ECG with motion artefacts,” In 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 1-4, 2022, doi: 10.1109/ITC-CSCC55581.2022.9894859.
B. R. Manju and M. R. Sneha, “ECG denoising using Wiener filter and Kalman filter”, in Third International Conference on Computing and Network Communications (CoCoNet’19), pp. 273-281, 2019, doi: 10.1016/j.procs.2020.04.029.
B. M. Manjula, R. Vivek, R. Nishant, N. S. Raja, M. Z. HN, and M. A. Goutham, “ECG denoising using multiple approaches,” 2023 International Conference on Data Science and Network Security (ICDSNS), pp. 1-6, 2023, doi: 10.1109/ICDSNS58469.2023.10245187.
S. Tahir, M. M. Raja, N. Razzaq, A. Mirza, W. Z. Khan, S. W. Kim, and Y. B. Zikria, “Extended Kalman filter-based power line interference canceller for electrocardiogram signal,” Big Data, vol. 10, no. 1, pp. 34-53, 2022, doi: 10.1089/big.2021.0043.
A. K. Roonizi and R. Sassi, “Smoothing filter design: a general framework,” Biomedical Signal Processing and Control, vol. 85, p. 104952, 2023, doi: 10.1016/j.bspc.2023.104952.
V. Murugan and D. Panigrahy, “Optimized adaptive filter for powerline interference cancellation in electrocardiogram signal using a modified lightning search algorithm,” Circuits, Systems, and Signal Processing, vol. 43, no. 10, pp. 6510-6536, 2024, doi:10.1007/s00034-024-02766-3.
N. D. Jahromi and H. D. Jahromi, “An investigation on the performance of infinite impulse response filters in denoising electrocardiogram signals,” Transactions on Machine Intelligence, vol. 6, no. 1, pp. 10-15, 2023, doi:10.47176/TMI.2023.10.
S. Basu and S. Mamud, “Comparative study on the effect of order and cut off frequency of Butterworth low pass filter for removal of noise in ECG signal,” In 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), pp. 156-160, 2020, doi:10.1109/ICCE50343.2020.9290646.
Y. S. Shmaliy, “Unbiased FIR filtering of discrete-time polynomial state-space models,” IEEE Transactions on Signal Processing, vol. 57, no. 4, pp. 1241-1249, 2009, doi: 10.1109/TSP.2008.2010640.
Y. S. Shmaliy and S. Zhao, “Optimal and robust state estimation: Finite Impulse Response (FIR) and Kalman approaches,” John Wiley & Sons, 2022, doi:10.1002/9781119863106.
C. Lastre-Domínguez, J. Muñoz-Minjares, E. Pérez-Campos, and Y. Shmaliy, “Denoising and time-frequency features extraction of ECG signals using iterative unbiased FIR algorithm,” In International Conference on Computing, Control and Industrial Engineering, Singapore: Springer Nature Singapore, pp. 308-316, 2024, doi:10.1007/978-981-97-6937-7_37.
J. U. Munoz-Minjares, M. Lopez-Ramirez, M. Vazquez-Olguin, C. Lastre-Dominguez, and Y. S. Shmaliy, “Outliers detection for accurate HRV-seizure baseline estimation using modern numerical algorithms,” Biomedical Signal Processing and Control, vol. 67, p. 102553, 2021, doi: 10.1016/j.bspc.2021.102553.
M. Khosravy, N. Gupta, N. Patel, T. Senjyu, and C. A. Duque, “Particle swarm optimization of morphological filters for electrocardiogram baseline drift estimation,” Applied Nature-Inspired Computing: Algorithms and Case Studies, pp. 1-21, 2020, doi: 10.1007/978-981-13-9263-4_1.
A. Osadchiy, A. Kamenev, V. Saharov, and S. Chernyi, “Signal processing algorithm based on discrete wavelet transform,” Designs, vol. 5, no. 3, p. 41, 2021, doi:10.3390/designs5030041.
M. A. Basarab, “The new wavelet-like Allan variance based on the atomic function,” In 2021 Photonics & Electromagnetics Research Symposium (PIERS), pp. 2870-2877, 2021, doi:10.1109/PIERS53385.2021.9694896.
R. C. Guido, “Wavelets behind the scenes: Practical aspects, insights, and perspectives,” Physics Reports, vol. 985, pp. 1-23, 2022, doi: 10.1016/j.physrep.2022.08.001.
S. Arfaoui, A. B. Mabrouk, and C. Cattani, “Wavelet analysis: basic concepts and applications,” Chapman and hall/CRC, 2021, doi: 10.1201/9781003096924.
C. Lastre-Domínguez, Y. S. Shmaliy, O. Ibarra-Manzano, J. Munoz-Minjares, and L. J. Morales-Mendoza, “ECG signal denoising and features extraction using unbiased FIR smoothing,” BioMed research international, vol. 2019, p. 2608547, 2019, doi:10.1155/2019/2608547.
A. John, J. Sadasivan, and C. S. Seelamantula, “Adaptive Savitzky-Golay filtering in non-Gaussian noise,” IEEE Transactions on Signal Processing, vol. 69, pp. 5021-5036, 2021, 10.1109/TSP.2021.3106450.
J. Dombi and A. Dineva, “Adaptive Savitzky-Golay filtering and its applications,” International Journal of Advanced Intelligence Paradigms, vol. 16, no. 2, pp.145-156, 2020, doi:10.1504/IJAIP.2020.107011.
N. B. Gallagher, “Savitzky-Golay smoothing and differentiation filter,” Eigenvector Research Incorporated, 2020, doi: 10.13140/RG.2.2.20339.50725.
P. E. McSharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, “A dynamical model for generating synthetic electrocardiogram signals,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 289-294, 2003, doi:10.1109/TBME.2003.808805.
J. J. A. Mendes Junior, D. P. Campos, L. C. D. A. V. D. Biassio, P. C. Passos, P. B. Júnior, A. E. Lazzaretti, and E. Krueger, “AD8232 to biopotentials sensors: Open source project and benchmark,” Electronics, vol. 12, no. 4, p. 833, 2023, doi:10.3390/electronics12040833.
Texas Instruments. ADS111x ultra-small, low-power, I2C-compatible, 860-SPS, 16 bit ADCs with internal reference, oscillator, and programmable comparator data sheet. Texas Instruments, 2009.
Texas Instrument. Analog Front-End Design for ECG Systems Using Delta-Sigma ADCs. Application Report, SBAA160A, 2009.
Y. Gan, W. Rahajandraibe, R. Vauche, B. Ravelo, N. Lorriere, and R. Bouchakour, “A new method to reduce motion artifact in electrocardiogram based on an innovative skin-electrode impedance model,” Biomedical Signal Processing and Control, vol. 76, p. 103640, 2022, doi: 10.1016/j.bspc.2022.103640.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Roberto Olivera-Reyna, Reynel Olivera-Reyna, Osbaldo Vite-Chavez, Rosa Janette Perez-Chimal, Jorge Ulises Munoz Minjares
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This journal is based on the work at https://journal.umy.ac.id/index.php/jrc under license from Creative Commons Attribution-ShareAlike 4.0 International License. You are free to:
- Share – copy and redistribute the material in any medium or format.
- Adapt – remix, transform, and build upon the material for any purpose, even comercially.
The licensor cannot revoke these freedoms as long as you follow the license terms, which include the following:
- Attribution. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
• Creative Commons Attribution-ShareAlike (CC BY-SA)
JRC is licensed under an International License
