Epileptic Seizure Detection Using a Recurrent Neural Network With Temporal Features Derived From a Scale Mixture EEG Model Article Swipe
Akira Furui
,
Ryota Onishi
,
Tomoyuki Akiyama
,
Toshio Tsuji
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3487637
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3487637
Automated detection of epileptic seizures from scalp Electroencephalogram (EEG) is crucial for improving epilepsy diagnosis and management. This paper presents an automated inter-patient epileptic seizure detection method using multichannel EEG signals. The proposed method uses a scale mixture-based stochastic EEG model for feature extraction and a recurrent neural network for seizure detection. Specifically, the stochastic model, which accounts for uncertainties in EEG amplitude, is fitted to a specific frequency band to extract relevant seizure features. Then, a recurrent neural network-based recognition architecture learns the temporal evolution of these features. We evaluated our method using EEG data from 20 patients with focal epilepsy and conducted comprehensive assessments, including ablation studies on classifiers and features. Our results demonstrate that our approach outperforms static classifiers and existing feature sets, achieving high sensitivity while maintaining acceptable specificity. Furthermore, our feature set showed efficacy both independently and as a complement to existing features, indicating its robustness in seizure detection tasks. These findings reveal that learning the temporal evolution of the stochastic fluctuation and amplitude information of EEG extracted using a stochastic model enables highly accurate seizure detection, potentially advancing automated epilepsy diagnosis in clinical settings.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3487637
- OA Status
- gold
- Cited By
- 4
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403863448
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403863448Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3487637Digital Object Identifier
- Title
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Epileptic Seizure Detection Using a Recurrent Neural Network With Temporal Features Derived From a Scale Mixture EEG ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Akira Furui, Ryota Onishi, Tomoyuki Akiyama, Toshio TsujiList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3487637Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2024.3487637Direct OA link when available
- Concepts
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Electroencephalography, Computer science, Epileptic seizure, Epilepsy, Artificial intelligence, Pattern recognition (psychology), Scale (ratio), Artificial neural network, Neuroscience, Psychology, Cartography, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.This | 17 |
| abstract_inverted_index.band | 69 |
| abstract_inverted_index.both | 139 |
| abstract_inverted_index.data | 95 |
| abstract_inverted_index.from | 5, 96 |
| abstract_inverted_index.high | 127 |
| abstract_inverted_index.that | 116, 158 |
| abstract_inverted_index.uses | 34 |
| abstract_inverted_index.with | 99 |
| abstract_inverted_index.(EEG) | 8 |
| abstract_inverted_index.Then, | 75 |
| abstract_inverted_index.These | 155 |
| abstract_inverted_index.focal | 100 |
| abstract_inverted_index.model | 40, 176 |
| abstract_inverted_index.paper | 18 |
| abstract_inverted_index.scale | 36 |
| abstract_inverted_index.scalp | 6 |
| abstract_inverted_index.sets, | 125 |
| abstract_inverted_index.these | 87 |
| abstract_inverted_index.using | 27, 93, 173 |
| abstract_inverted_index.which | 56 |
| abstract_inverted_index.while | 129 |
| abstract_inverted_index.fitted | 64 |
| abstract_inverted_index.highly | 178 |
| abstract_inverted_index.learns | 82 |
| abstract_inverted_index.method | 26, 33, 92 |
| abstract_inverted_index.model, | 55 |
| abstract_inverted_index.neural | 47, 78 |
| abstract_inverted_index.reveal | 157 |
| abstract_inverted_index.showed | 137 |
| abstract_inverted_index.static | 120 |
| abstract_inverted_index.tasks. | 154 |
| abstract_inverted_index.crucial | 10 |
| abstract_inverted_index.enables | 177 |
| abstract_inverted_index.extract | 71 |
| abstract_inverted_index.feature | 42, 124, 135 |
| abstract_inverted_index.network | 48 |
| abstract_inverted_index.results | 114 |
| abstract_inverted_index.seizure | 24, 50, 73, 152, 180 |
| abstract_inverted_index.studies | 108 |
| abstract_inverted_index.ablation | 107 |
| abstract_inverted_index.accounts | 57 |
| abstract_inverted_index.accurate | 179 |
| abstract_inverted_index.approach | 118 |
| abstract_inverted_index.clinical | 188 |
| abstract_inverted_index.efficacy | 138 |
| abstract_inverted_index.epilepsy | 13, 101, 185 |
| abstract_inverted_index.existing | 123, 146 |
| abstract_inverted_index.findings | 156 |
| abstract_inverted_index.learning | 159 |
| abstract_inverted_index.patients | 98 |
| abstract_inverted_index.presents | 19 |
| abstract_inverted_index.proposed | 32 |
| abstract_inverted_index.relevant | 72 |
| abstract_inverted_index.seizures | 4 |
| abstract_inverted_index.signals. | 30 |
| abstract_inverted_index.specific | 67 |
| abstract_inverted_index.temporal | 84, 161 |
| abstract_inverted_index.Automated | 0 |
| abstract_inverted_index.achieving | 126 |
| abstract_inverted_index.advancing | 183 |
| abstract_inverted_index.amplitude | 168 |
| abstract_inverted_index.automated | 21, 184 |
| abstract_inverted_index.conducted | 103 |
| abstract_inverted_index.detection | 1, 25, 153 |
| abstract_inverted_index.diagnosis | 14, 186 |
| abstract_inverted_index.epileptic | 3, 23 |
| abstract_inverted_index.evaluated | 90 |
| abstract_inverted_index.evolution | 85, 162 |
| abstract_inverted_index.extracted | 172 |
| abstract_inverted_index.features, | 147 |
| abstract_inverted_index.features. | 74, 88, 112 |
| abstract_inverted_index.frequency | 68 |
| abstract_inverted_index.improving | 12 |
| abstract_inverted_index.including | 106 |
| abstract_inverted_index.recurrent | 46, 77 |
| abstract_inverted_index.settings. | 189 |
| abstract_inverted_index.acceptable | 131 |
| abstract_inverted_index.amplitude, | 62 |
| abstract_inverted_index.complement | 144 |
| abstract_inverted_index.detection, | 181 |
| abstract_inverted_index.detection. | 51 |
| abstract_inverted_index.extraction | 43 |
| abstract_inverted_index.indicating | 148 |
| abstract_inverted_index.robustness | 150 |
| abstract_inverted_index.stochastic | 38, 54, 165, 175 |
| abstract_inverted_index.classifiers | 110, 121 |
| abstract_inverted_index.demonstrate | 115 |
| abstract_inverted_index.fluctuation | 166 |
| abstract_inverted_index.information | 169 |
| abstract_inverted_index.maintaining | 130 |
| abstract_inverted_index.management. | 16 |
| abstract_inverted_index.outperforms | 119 |
| abstract_inverted_index.potentially | 182 |
| abstract_inverted_index.recognition | 80 |
| abstract_inverted_index.sensitivity | 128 |
| abstract_inverted_index.Furthermore, | 133 |
| abstract_inverted_index.architecture | 81 |
| abstract_inverted_index.assessments, | 105 |
| abstract_inverted_index.multichannel | 28 |
| abstract_inverted_index.specificity. | 132 |
| abstract_inverted_index.Specifically, | 52 |
| abstract_inverted_index.comprehensive | 104 |
| abstract_inverted_index.independently | 140 |
| abstract_inverted_index.inter-patient | 22 |
| abstract_inverted_index.mixture-based | 37 |
| abstract_inverted_index.network-based | 79 |
| abstract_inverted_index.uncertainties | 59 |
| abstract_inverted_index.Electroencephalogram | 7 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.86042504 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |