Short-horizon neonatal seizure prediction using EEG-based deep learning.

Journal: PLOS digital health
Published Date: Jul 11, 2025

Abstract

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.

Authors

  • Jonathan Kim
    Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.
  • Edilberto Amorim
    Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: edilbertoamorim@gmail.com.
  • Vikram R Rao
    Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
  • Hannah C Glass
    Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
  • Danilo Bernardo
    Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.

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