An overview of machine learning and deep learning techniques for predicting epileptic seizures
Abstract Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection and prediction of epileptic seizures. The objective of this study is to portray the existing machine learning methods for seizure prediction. Internet bibliographical searches were conducted to identify relevant literature on the topic. Through cross-referencing from key articles, additional references were obtained to provide a comprehensive overview of the techniques. As the aim of this paper aims is not a pure bibliographical review of the subject, the publications here cited have been selected among many others based on their number of citations. To implement accurate diagnostic and treatment tools, it is necessary to achieve a balance between prediction time, sensitivity, and specificity. This balance can be achieved using deep learning algorithms. The best performance and results are often achieved by combining multiple techniques and features, but this approach can also increase computational requirements..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:20 |
|---|---|
| Enthalten in: |
Journal of integrative bioinformatics - 20(2023), 4 vom: 15. Dez. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zurdo-Tabernero, Marco [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| BKL: |
|---|
| Anmerkungen: |
© 2023 the author(s), published by De Gruyter, Berlin/Boston |
|---|
| doi: |
10.1515/jib-2023-0002 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
GRUY009387579 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | GRUY009387579 | ||
| 003 | DE-627 | ||
| 005 | 20240111003619.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240111s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1515/jib-2023-0002 |2 doi | |
| 035 | |a (DE-627)GRUY009387579 | ||
| 035 | |a (DE-B1597)jib-2023-0002-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 570 |a 004 |q VZ |
| 082 | 0 | 4 | |a 570 |a 004 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 42.11 |2 bkl | ||
| 084 | |a 58.30 |2 bkl | ||
| 100 | 1 | |a Zurdo-Tabernero, Marco |e verfasserin |0 (orcid)0000-0002-6381-5149 |4 aut | |
| 245 | 1 | 0 | |a An overview of machine learning and deep learning techniques for predicting epileptic seizures |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © 2023 the author(s), published by De Gruyter, Berlin/Boston | ||
| 520 | |a Abstract Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection and prediction of epileptic seizures. The objective of this study is to portray the existing machine learning methods for seizure prediction. Internet bibliographical searches were conducted to identify relevant literature on the topic. Through cross-referencing from key articles, additional references were obtained to provide a comprehensive overview of the techniques. As the aim of this paper aims is not a pure bibliographical review of the subject, the publications here cited have been selected among many others based on their number of citations. To implement accurate diagnostic and treatment tools, it is necessary to achieve a balance between prediction time, sensitivity, and specificity. This balance can be achieved using deep learning algorithms. The best performance and results are often achieved by combining multiple techniques and features, but this approach can also increase computational requirements. | ||
| 700 | 1 | |a Canal-Alonso, Ángel |0 (orcid)0000-0002-4254-5944 |4 aut | |
| 700 | 1 | |a de la Prieta, Fernando |0 (orcid)0000-0002-8239-5020 |4 aut | |
| 700 | 1 | |a Rodríguez, Sara |0 (orcid)0000-0002-3081-5177 |4 aut | |
| 700 | 1 | |a Prieto, Javier |0 (orcid)0000-0001-8175-2201 |4 aut | |
| 700 | 1 | |a Corchado, Juan Manuel |0 (orcid)0000-0002-2829-1829 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of integrative bioinformatics |d De Gruyter, 2004 |g 20(2023), 4 vom: 15. Dez. |h Online-Ressource |w (DE-627)GRUY000251003 |w (DE-600)2147212-9 |w (DE-576)28125124X |x 1613-4516 |7 nnns |
| 773 | 1 | 8 | |g volume:20 |g year:2023 |g number:4 |g day:15 |g month:12 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1515/jib-2023-0002 |z kostenfrei |3 Volltext |
| 912 | |a GBV_GRUY | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a SSG-OPC-MAT | ||
| 936 | b | k | |a 42.11 |j Biomathematik |j Biokybernetik |q VZ |
| 936 | b | k | |a 58.30 |j Biotechnologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 20 |j 2023 |e 4 |b 15 |c 12 | ||