Enhanced Non-EEG Multimodal Seizure Detection: A Real-World Model for Identifying Generalised Seizures Across the...

Non-electroencephalogram seizure detection holds promise for the early identification of generalised onset seizures. However, existing methods often suffer from high false alarm rates and difficulty distinguishing normal movements from seizure manifestations. To address this, we obtained exclusive access to the Open Seizure Database and selected a representative dataset of 94 events (42 generalised tonic-clonic seizures, 19 focal seizures, and 33 labelled as Other), totaling approximately 5 hours and 29 minutes. Each event contains acceleration and heart rate data, which were expertly annotated by a clinician in 5 second timesteps, with each timestep assigned a class label of Normal, Pre-Ictal, or Ictal. We introduce AMBER (Attention-guided Multi-Branching pipeline with Enhanced Residual Fusion), a multimodal seizure detection model designed for Ictal-Phase Detection. AMBER constructs multiple branches to form independent feature extraction pipelines for each sensing modality. The outputs of each branch are passed to a Residual Fusion layer, where the extracted features are combined into a fused representation and propagated through two densely connected blocks. The results of these experiments highlight the effectiveness of Ictal Phase Detection, with the model recording an accuracy and f1-score of 0.9027 and 0.9035, respectively, on unseen test data. Further experiments recorded True Positive Rate of 0.8342, 0.9485, and 0.9118 for the Normal, Pre-Ictal, and Ictal phases, respectively, with an average False Positive Rate of 0.0502. This study presents a novel Ictal Phase Detection technique that enhances seizure phase classification while showing reduced false alarms, laying the groundwork for further advancements in non-electroencephalogram-based seizure detection research.