The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network
High frequency oscillations are important novel biomarkers of epileptic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. Power-to-power coupling (PPC...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1513661/full |
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| author | A.V. Medvedev B. Lehmann |
| author_facet | A.V. Medvedev B. Lehmann |
| author_sort | A.V. Medvedev |
| collection | DOAJ |
| description | High frequency oscillations are important novel biomarkers of epileptic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. Power-to-power coupling (PPC) is one form of coupling with significant research attesting to its neurobiological significance as well as its computational efficiency, yet has been hitherto unexplored within seizure classification literature. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to classify absence seizure activity based on this important form of cross-frequency patterns within scalp EEG. The analysis is done on the EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Power-to-power coupling was calculated between all frequencies 2–120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 93.1%, specificity of 99.5% and overall accuracy of 96.8%. The results provide evidence both for (1) the relevance of PPC for seizure classification, as well as (2) the efficacy of an approach combining PPC with SSAE neural networks for automated classification of absence seizures within scalp EEG. |
| format | Article |
| id | doaj-art-b016deeb9cd94a7b8b080bbab68e0300 |
| institution | Kabale University |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-b016deeb9cd94a7b8b080bbab68e03002025-02-10T06:48:55ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-02-011910.3389/fninf.2025.15136611513661The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning networkA.V. MedvedevB. LehmannHigh frequency oscillations are important novel biomarkers of epileptic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. Power-to-power coupling (PPC) is one form of coupling with significant research attesting to its neurobiological significance as well as its computational efficiency, yet has been hitherto unexplored within seizure classification literature. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to classify absence seizure activity based on this important form of cross-frequency patterns within scalp EEG. The analysis is done on the EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Power-to-power coupling was calculated between all frequencies 2–120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 93.1%, specificity of 99.5% and overall accuracy of 96.8%. The results provide evidence both for (1) the relevance of PPC for seizure classification, as well as (2) the efficacy of an approach combining PPC with SSAE neural networks for automated classification of absence seizures within scalp EEG.https://www.frontiersin.org/articles/10.3389/fninf.2025.1513661/fullabsence seizureepilepsyseizure classificationEEGspectral analysiscross-frequency coupling (CFC) |
| spellingShingle | A.V. Medvedev B. Lehmann The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network Frontiers in Neuroinformatics absence seizure epilepsy seizure classification EEG spectral analysis cross-frequency coupling (CFC) |
| title | The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network |
| title_full | The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network |
| title_fullStr | The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network |
| title_full_unstemmed | The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network |
| title_short | The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network |
| title_sort | classification of absence seizures using power to power cross frequency coupling analysis with a deep learning network |
| topic | absence seizure epilepsy seizure classification EEG spectral analysis cross-frequency coupling (CFC) |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1513661/full |
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