Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection
Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises. Metho...
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| Format: | Article |
| Language: | Spanish |
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Universidad Distrital Francisco José de Caldas
2025-04-01
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| Series: | Ingeniería |
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| Online Access: | https://revistas.udistrital.edu.co/index.php/reving/article/view/22185 |
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| author | Luis Daladier Guerrero Otoya Maximiliano Bueno López Eduardo Giraldo Suárez Marta Molinas Cabrera |
| author_facet | Luis Daladier Guerrero Otoya Maximiliano Bueno López Eduardo Giraldo Suárez Marta Molinas Cabrera |
| author_sort | Luis Daladier Guerrero Otoya |
| collection | DOAJ |
| description | Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises.
Method: This study addressed a significant gap by proposing a novel approach for the automated selection of the most relevant intrinsic mode functions (IMFs) using empirical mode decomposition and discrimination metrics such as the Minkowski distance, the mean square error, cross-correlation, and the entropy function. The main objective was to address the challenge of determining the optimal number of IMFs required to accurately reconstruct brain activity signals.
Results:The results were promising, as they facilitated the identification of IMFs that contained the most relevant information, marking a significant advancement in the field. To validate these findings, standard methods including the correlation coefficient, the p-value, and the Wasserstein distance were employed. Additionally, an EEGLAB-based brain mapping was conducted, adding robustness and credibility to the results obtained.
Conclusions: Our method is a fundamental tool that enhances epileptic seizure identification from EEG signals, with significant clinical implications in the diagnosis and treatment of epilepsy. |
| format | Article |
| id | doaj-art-8cc7ca29ddbb465cb574bfb4b20ad04c |
| institution | Kabale University |
| issn | 0121-750X 2344-8393 |
| language | Spanish |
| publishDate | 2025-04-01 |
| publisher | Universidad Distrital Francisco José de Caldas |
| record_format | Article |
| series | Ingeniería |
| spelling | doaj-art-8cc7ca29ddbb465cb574bfb4b20ad04c2025-08-20T03:47:05ZspaUniversidad Distrital Francisco José de CaldasIngeniería0121-750X2344-83932025-04-01301e22185e2218510.14483/23448393.2218521023Optimal Selection of Intrinsic Mode Functions Applied to Seizure DetectionLuis Daladier Guerrero Otoya0https://orcid.org/0000-0003-4690-4569Maximiliano Bueno López1https://orcid.org/0000-0002-7959-9962Eduardo Giraldo Suárez2Marta Molinas Cabrera3University of Cauca Technological University of Pereira Technological University of Pereira Norwegian University of Science and Technology Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises. Method: This study addressed a significant gap by proposing a novel approach for the automated selection of the most relevant intrinsic mode functions (IMFs) using empirical mode decomposition and discrimination metrics such as the Minkowski distance, the mean square error, cross-correlation, and the entropy function. The main objective was to address the challenge of determining the optimal number of IMFs required to accurately reconstruct brain activity signals. Results:The results were promising, as they facilitated the identification of IMFs that contained the most relevant information, marking a significant advancement in the field. To validate these findings, standard methods including the correlation coefficient, the p-value, and the Wasserstein distance were employed. Additionally, an EEGLAB-based brain mapping was conducted, adding robustness and credibility to the results obtained. Conclusions: Our method is a fundamental tool that enhances epileptic seizure identification from EEG signals, with significant clinical implications in the diagnosis and treatment of epilepsy.https://revistas.udistrital.edu.co/index.php/reving/article/view/22185seizure identificationempirical mode decompositionoptimal selection of imfsintrinsic mode functionsdiscrimination metrics |
| spellingShingle | Luis Daladier Guerrero Otoya Maximiliano Bueno López Eduardo Giraldo Suárez Marta Molinas Cabrera Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection Ingeniería seizure identification empirical mode decomposition optimal selection of imfs intrinsic mode functions discrimination metrics |
| title | Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection |
| title_full | Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection |
| title_fullStr | Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection |
| title_full_unstemmed | Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection |
| title_short | Optimal Selection of Intrinsic Mode Functions Applied to Seizure Detection |
| title_sort | optimal selection of intrinsic mode functions applied to seizure detection |
| topic | seizure identification empirical mode decomposition optimal selection of imfs intrinsic mode functions discrimination metrics |
| url | https://revistas.udistrital.edu.co/index.php/reving/article/view/22185 |
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