Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles
Although electroencephalogram (EEG) is widely used to monitor brain activity in epilepsy, limitations related to the accessibility and reproducibility of measurements may restrict its everyday use. Conversely, wearable methods, easily accessible, such as electrocardiogram (ECG), represent an alterna...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1555162/full |
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| author | Cândida H. L. Alves Gilberto S. Alves Gilberto S. Alves Rômulo Kunrath Mariélia Barbosa L. de Freitas João Pedro G. Castor Allan Kardec Barros Diego Dutra Sampaio Jonathan Araújo Queiroz |
| author_facet | Cândida H. L. Alves Gilberto S. Alves Gilberto S. Alves Rômulo Kunrath Mariélia Barbosa L. de Freitas João Pedro G. Castor Allan Kardec Barros Diego Dutra Sampaio Jonathan Araújo Queiroz |
| author_sort | Cândida H. L. Alves |
| collection | DOAJ |
| description | Although electroencephalogram (EEG) is widely used to monitor brain activity in epilepsy, limitations related to the accessibility and reproducibility of measurements may restrict its everyday use. Conversely, wearable methods, easily accessible, such as electrocardiogram (ECG), represent an alternative for indirectly monitoring brain activity through cardiac cycles. A computational model was developed based on statistical cycles and neural networks to measure changes in the morphology of ECG waves. The advantage of this approach over heart rate variability analysis is the detection of brain activity before changes in heart rate occur. In addition, using variance, skewness, and kurtosis centered on the median allowed us to achieve 100% sensitivity, specificity, and accuracy in our analyses, even using less complex algorithms, due to selecting these optimal characteristics. These findings indicate that ECG is a viable, affordable, and effective alternative for estimating epileptic brain activity. This approach’s application of machine learning highlights its potential for non-invasive epilepsy monitoring, providing a cost-effective and accessible solution, especially for vulnerable populations. |
| format | Article |
| id | doaj-art-f7f6dfef9f5a4d97a179e5bbba0b39b9 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-f7f6dfef9f5a4d97a179e5bbba0b39b92025-08-20T03:34:52ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-07-011610.3389/fneur.2025.15551621555162Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cyclesCândida H. L. Alves0Gilberto S. Alves1Gilberto S. Alves2Rômulo Kunrath3Mariélia Barbosa L. de Freitas4João Pedro G. Castor5Allan Kardec Barros6Diego Dutra Sampaio7Jonathan Araújo Queiroz8Department of Psychology, Edufor Faculty, Idomed Faculty, São Luís, Maranhão, BrazilNeuropsychiatry Unit, Nina Rodrigues Hospital, São Luís, Maranhão, BrazilDepartment of Medicine I, Federal University of Maranhão, São Luís, Maranhão, BrazilNeuropsychiatry Unit, Nina Rodrigues Hospital, São Luís, Maranhão, BrazilFederal University of Ceará, Fortaleza, Ceará, BrazilFederal University of Maranhão, Imperatriz, BrazilElectrical Engineering Department, Federal University of Maranhão, São Luís, Maranhão, BrazilElectrical Engineering Department, Federal University of Maranhão, São Luís, Maranhão, BrazilElectrical Engineering Department, Federal University of Maranhão, São Luís, Maranhão, BrazilAlthough electroencephalogram (EEG) is widely used to monitor brain activity in epilepsy, limitations related to the accessibility and reproducibility of measurements may restrict its everyday use. Conversely, wearable methods, easily accessible, such as electrocardiogram (ECG), represent an alternative for indirectly monitoring brain activity through cardiac cycles. A computational model was developed based on statistical cycles and neural networks to measure changes in the morphology of ECG waves. The advantage of this approach over heart rate variability analysis is the detection of brain activity before changes in heart rate occur. In addition, using variance, skewness, and kurtosis centered on the median allowed us to achieve 100% sensitivity, specificity, and accuracy in our analyses, even using less complex algorithms, due to selecting these optimal characteristics. These findings indicate that ECG is a viable, affordable, and effective alternative for estimating epileptic brain activity. This approach’s application of machine learning highlights its potential for non-invasive epilepsy monitoring, providing a cost-effective and accessible solution, especially for vulnerable populations.https://www.frontiersin.org/articles/10.3389/fneur.2025.1555162/fullepilepsyseizuresmachine learningECGheart rate variabilityneural network |
| spellingShingle | Cândida H. L. Alves Gilberto S. Alves Gilberto S. Alves Rômulo Kunrath Mariélia Barbosa L. de Freitas João Pedro G. Castor Allan Kardec Barros Diego Dutra Sampaio Jonathan Araújo Queiroz Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles Frontiers in Neurology epilepsy seizures machine learning ECG heart rate variability neural network |
| title | Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles |
| title_full | Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles |
| title_fullStr | Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles |
| title_full_unstemmed | Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles |
| title_short | Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles |
| title_sort | neural network based method for measuring the impacts of epileptic brain activities on cardiac cycles |
| topic | epilepsy seizures machine learning ECG heart rate variability neural network |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1555162/full |
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