Short-horizon neonatal seizure prediction using EEG-based deep learning.
Strategies to predict neonatal seizure risk have typically focused on long-term static predictions with prediction horizons spanning days during the acute postnatal period. Higher temporal resolution or short-horizon neonatal seizure prediction, on the time-frame of minutes, remains unexplored. Here...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-07-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000890 |
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| _version_ | 1850099603490209792 |
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| author | Jonathan Kim Edilberto Amorim Vikram R Rao Hannah C Glass Danilo Bernardo |
| author_facet | Jonathan Kim Edilberto Amorim Vikram R Rao Hannah C Glass Danilo Bernardo |
| author_sort | Jonathan Kim |
| collection | DOAJ |
| description | Strategies to predict neonatal seizure risk have typically focused on long-term static predictions with prediction horizons spanning days during the acute postnatal period. Higher temporal resolution or short-horizon neonatal seizure prediction, on the time-frame of minutes, remains unexplored. Here, we investigated quantitative electroencephalography (QEEG) based deep learning (DL) for short-horizon seizure prediction. We used two publicly available EEG seizure datasets with a total of 132 neonates containing a total of 281 hours of EEG data. We benchmarked current state-of-the-art time-series DL methods for seizure prediction, identifying convolutional LSTM (ConvLSTM) as having the strongest performance at preictal state classification. We assessed ConvLSTM performance in a seizure alarm system over varying short-range (1-7 minutes) seizure prediction horizons (SPH) and seizure occurrence periods (SOP) and identified optimal performance at SPH 3 min and SOP 7 min, with AUROC 0.8. At 80% sensitivity, false detection rate was 0.68 events/hour with time-in-warning of 0.36. Model calibration was moderate, with an expected calibration error of 0.106. These findings establish the feasibility of short-horizon neonatal seizure prediction and warrant the need for further validation. |
| format | Article |
| id | doaj-art-51610a119aac42ff8094690cbff10fdb |
| institution | DOAJ |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-51610a119aac42ff8094690cbff10fdb2025-08-20T02:40:27ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-07-0147e000089010.1371/journal.pdig.0000890Short-horizon neonatal seizure prediction using EEG-based deep learning.Jonathan KimEdilberto AmorimVikram R RaoHannah C GlassDanilo BernardoStrategies to predict neonatal seizure risk have typically focused on long-term static predictions with prediction horizons spanning days during the acute postnatal period. Higher temporal resolution or short-horizon neonatal seizure prediction, on the time-frame of minutes, remains unexplored. Here, we investigated quantitative electroencephalography (QEEG) based deep learning (DL) for short-horizon seizure prediction. We used two publicly available EEG seizure datasets with a total of 132 neonates containing a total of 281 hours of EEG data. We benchmarked current state-of-the-art time-series DL methods for seizure prediction, identifying convolutional LSTM (ConvLSTM) as having the strongest performance at preictal state classification. We assessed ConvLSTM performance in a seizure alarm system over varying short-range (1-7 minutes) seizure prediction horizons (SPH) and seizure occurrence periods (SOP) and identified optimal performance at SPH 3 min and SOP 7 min, with AUROC 0.8. At 80% sensitivity, false detection rate was 0.68 events/hour with time-in-warning of 0.36. Model calibration was moderate, with an expected calibration error of 0.106. These findings establish the feasibility of short-horizon neonatal seizure prediction and warrant the need for further validation.https://doi.org/10.1371/journal.pdig.0000890 |
| spellingShingle | Jonathan Kim Edilberto Amorim Vikram R Rao Hannah C Glass Danilo Bernardo Short-horizon neonatal seizure prediction using EEG-based deep learning. PLOS Digital Health |
| title | Short-horizon neonatal seizure prediction using EEG-based deep learning. |
| title_full | Short-horizon neonatal seizure prediction using EEG-based deep learning. |
| title_fullStr | Short-horizon neonatal seizure prediction using EEG-based deep learning. |
| title_full_unstemmed | Short-horizon neonatal seizure prediction using EEG-based deep learning. |
| title_short | Short-horizon neonatal seizure prediction using EEG-based deep learning. |
| title_sort | short horizon neonatal seizure prediction using eeg based deep learning |
| url | https://doi.org/10.1371/journal.pdig.0000890 |
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