# A New Neuromorphic Computing Approach for Epileptic Seizure Prediction

arXiv: https://arxiv.org/abs/2102.12773 · HTML: https://mlsanity.com/paper/2102.12773/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Convolutional Neural Networks for Epileptic Seizure Prediction](https://mlsanity.com/paper/1811.00915/) (2018)
- [Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction](https://mlsanity.com/paper/1904.03603/) (2019)

## Built on by (most cited first, 5 of 5)

- [Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer](https://mlsanity.com/paper/2402.09424/) (2024)
- [SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network](https://mlsanity.com/paper/2410.09998/) (2024)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [On using AI for EEG-based BCI applications: problems, current challenges and future trends](https://mlsanity.com/paper/2506.16168/) (2025)
- [Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks](https://mlsanity.com/paper/2607.16296/) (2026)

## Abstract (arXiv)

Several high specificity and sensitivity seizure prediction methods with convolutional neural networks (CNNs) are reported. However, CNNs are computationally expensive and power hungry. These inconveniences make CNN-based methods hard to be implemented on wearable devices. Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work. This approach uses a designed gaussian random discrete encoder to generate spike sequences from the EEG samples and make predictions in a spiking convolutional neural network (Spiking-CNN) which combines the advantages of CNNs and SNNs. The experimental results show that the sensitivity, specificity and AUC can remain 95.1%, 99.2% and 0.912 respectively while the computation complexity is reduced by 98.58% compared to CNN, indicating that the proposed Spiking-CNN is hardware friendly and of high precision.