# Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection

arXiv: https://arxiv.org/abs/2304.06038 · HTML: https://mlsanity.com/paper/2304.06038/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Distilling Knowledge from Graph Convolutional Networks](https://mlsanity.com/paper/2003.10477/) (2020)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Graph-Free Knowledge Distillation for Graph Neural Networks](https://mlsanity.com/paper/2105.07519/) (2021)
- [On Representation Knowledge Distillation for Graph Neural Networks](https://mlsanity.com/paper/2111.04964/) (2021)
- [On Self-Distilling Graph Neural Network](https://mlsanity.com/paper/2011.02255/) (2020)
- [Distilling Holistic Knowledge with Graph Neural Networks](https://mlsanity.com/paper/2108.05507/) (2021)
- [FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks](https://mlsanity.com/paper/2206.06561/) (2022)
- [Temporal Graph Convolutional Networks for Automatic Seizure Detection](https://mlsanity.com/paper/1905.01375/) (2019)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax](https://mlsanity.com/paper/2102.09050/) (2021)
- [Convolutional neural network for detection and classification of seizures in clinical data](https://mlsanity.com/paper/1903.08864/) (2019)
- [Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals](https://mlsanity.com/paper/2012.11933/) (2020)
- [A Meta-GNN approach to personalized seizure detection and classification](https://mlsanity.com/paper/2211.02642/) (2022)

## Built on by (most cited first, 1 of 1)

- [MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection](https://mlsanity.com/paper/2503.08251/) (2025)

## Abstract (arXiv)

Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student). They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. We consider the case where the teacher and the student seizure detectors are graph neural networks (GNN), since these architectures actively use the connectivity information. We consider two cases (a) when a single student is learnt for all the patients using preselected channels; and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbelsoftmax approach. Our experiments on the publicly available Temple University Hospital EEG Seizure Data Corpus (TUSZ) show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data. We observe that using as few as two channels, we are able to obtain competitive seizure detection performance. This, in turn, shows the potential of our approach in more realistic scenario of wearable devices for personalized monitoring of seizures, even with few recordings.