# Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection

arXiv: https://arxiv.org/abs/2111.08463 · HTML: https://mlsanity.com/paper/2111.08463/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 16 Nov 2021.

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

- [One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing](https://mlsanity.com/paper/1809.01926/) (2018)
- [Objective evaluation metrics for automatic classification of EEG events](https://mlsanity.com/paper/1712.10107/) (2017)
- [Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection](https://mlsanity.com/paper/2105.00934/) (2021)

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

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration](https://mlsanity.com/paper/2206.04746/) (2022)
- [Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection](https://mlsanity.com/paper/2201.09759/) (2022)
- [Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection](https://mlsanity.com/paper/2205.07654/) (2022)
- [Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing](https://mlsanity.com/paper/2303.14745/) (2023)

## Abstract (arXiv)

Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new solutions that allow continuous unobstructed monitoring and reliable detection and prediction of seizures. A high variability in the electroencephalogram (EEG) patterns exists among people, brain states, and time instances during seizures, but also during non-seizure periods. This makes epileptic seizure detection very challenging, especially if data is grouped under only seizure and non-seizure labels. Hyperdimensional (HD) computing, a novel machine learning approach, comes in as a promising tool. However, it has certain limitations when the data shows a high intra-class variability. Therefore, in this work, we propose a novel semi-supervised learning approach based on a multi-centroid HD computing. The multi-centroid approach allows to have several prototype vectors representing seizure and non-seizure states, which leads to significantly improved performance when compared to a simple 2-class HD model. Further, real-life data imbalance poses an additional challenge and the performance reported on balanced subsets of data is likely to be overestimated. Thus, we test our multi-centroid approach with three different dataset balancing scenarios, showing that performance improvement is higher for the less balanced dataset. More specifically, up to 14% improvement is achieved on an unbalanced test set with 10 times more non-seizure than seizure data. At the same time, the total number of sub-classes is not significantly increased compared to the balanced dataset. Thus, the proposed multi-centroid approach can be an important element in achieving a high performance of epilepsy detection with real-life data balance or during online learning, where seizures are infrequent.