Yang, Yang
Luan, Tianyun
Yu, Zhangjun
Zhang, Min
Li, Fengtian
Chen, Xing
Gao, Fei
Zhang, Zhijun
Funding for this research was provided by:
the National Natural Science Foundation of China, Ye Qisun Foundation (U2141231)
the Fund of Education Department of Jilin Province (JJKH20241673KJ)
Jilin Science and Technology Development Program Project (20230201076GX)
Article History
Received: 30 November 2023
Accepted: 1 February 2024
First Online: 16 February 2024
Declarations
:
: In our study, we employed two distinct datasets for our analysis. The first dataset was the SWEC-ETHZ dataset, which focuses on brain epilepsy. The second dataset we utilized was the CHB-MIT dataset, which is also centred around brain epilepsy. These datasets were instrumental in providing comprehensive insights into our research topic. The SWEC-ETHZ dataset is renowned for its detailed and extensive brain epilepsy data. It includes a wide range of parameters, such as EEG readings, patient demographics, and clinical outcomes. This dataset has been widely used in the medical research community due to its reliability and the depth of information it provides, making it a valuable resource for our study. On the other hand, the CHB-MIT dataset offers a unique perspective on brain epilepsy, featuring a diverse collection of case studies. It encompasses various aspects of epilepsy, including different age groups and epilepsy types. This dataset is particularly notable for its applicability in developing predictive models and for its contribution to understanding the complex nature of epilepsy. In our study, we strictly abided by the usage regulations set by the providers of these datasets, ensuring that the data were used solely for noncommercial scientific research. Moreover, we respect the confidentiality and anonymity of all data, ensuring that no personally identifiable information was disclosed in the course of our research.
: We, the undersigned authors of the manuscript titled “Technological Vanguard: The Outstanding Performance of the LTY-CNN Model for the Early Prediction of Epileptic Seizures”, confirm our consent to publish this work. We affirm that we have all contributed significantly to the research and the preparation of the manuscript, and we have approved the final version to be published. We agree that this manuscript is original, has not been previously published, and is not under consideration for publication elsewhere. We collectively take responsibility for the integrity of the work as a whole.
: We hereby declare that there are no financial conflicts of interest or authorship disputes related to this manuscript. No funding has influenced the outcome of this study, and all authors have collaboratively and harmoniously contributed to this work.