Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection

TL;DR

This paper introduces a multi-centroid hyperdimensional computing method for epileptic seizure detection, improving accuracy in real-world, imbalanced EEG datasets by representing multiple seizure and non-seizure states.

Contribution

It proposes a novel semi-supervised multi-centroid hyperdimensional approach that enhances seizure detection performance, especially in unbalanced datasets, compared to traditional 2-class models.

Findings

Up to 14% performance improvement on unbalanced data

Effective in real-life imbalanced EEG datasets

Maintains manageable number of subclasses

Abstract

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.