Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer
This work addresses timely seizure prediction for epilepsy patients to reduce injury, but it is incremental as it builds on existing neuromorphic and transformer methods.
The paper tackles epilepsy seizure detection and prediction from EEG data using a neuromorphic Spiking Convolutional Transformer, achieving high sensitivity (e.g., 94.9% for detection) and specificity (e.g., 99.3% for detection) while reducing computational costs by over 10x compared to non-spiking models.
Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a neuromorphic Spiking Convolutional Transformer, named Spiking Conformer, to detect and predict epileptic seizure segments from scalped long-term electroencephalogram (EEG) recordings. We report evaluation results from the Spiking Conformer model using the Boston Children's Hospital-MIT (CHB-MIT) EEG dataset. By leveraging spike-based addition operations, the Spiking Conformer significantly reduces the classification computational cost compared to the non-spiking model. Additionally, we introduce an approximate spiking neuron layer to further reduce spike-triggered neuron updates by nearly 38% without sacrificing accuracy. Using raw EEG data as input, the proposed Spiking Conformer achieved an average sensitivity rate of 94.9% and a specificity rate of 99.3% for the seizure detection task, and 96.8%, 89.5% for the seizure prediction task, and needs >10x fewer operations compared to the non-spiking equivalent model.