stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
-  A. S. Zandi, R. Tafreshi, M. Javidan and G. A. Dumont, “Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals,” IEEE Transactions on Biomedical Engineering, vol. 60, NO. 5, MAY 2013.
-  A. S. Zandi, R. Tafreshi, M. Javidan and G. A. Dumont, “Predicting Temporal Lobe Epileptic Seizures Based on Zero–Crossing Interval Analysis in Scalp EEG,” 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010.
-  A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody and C.-K. Peng, and H. Stanley, “PhysioBank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, pp. e215–e220, 2000.
-  H. Berger, “Archiv für Psychiatrie und Nervenkrankheiten”, 87: 527-570, 1929.
-  LeVan, P., Urrestarazu, E. and Gotman, J. (2006). A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classiﬁcation. Clinical Neurophysiology, 117, 912–927.
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