Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

TL;DR

This paper systematically evaluates hyperdimensional computing methods for epileptic seizure detection, comparing feature approaches across datasets, focusing on detection accuracy, memory, and computational efficiency for wearable use.

Contribution

It provides a comprehensive, standardized assessment of HD computing approaches for seizure detection, including novel features and post-processing strategies, across multiple datasets.

Findings

Significant performance differences between approaches.

High-performing methods may be unsuitable for wearables due to resource demands.

Post-processing improves detection accuracy and reduces approach disparities.

Abstract

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparation, segmentation, encoding strategies, and performance metrics, results are hard to compare, which makes building upon that knowledge difficult. Thus, the main goal of this work is to perform a systematic assessment of the HD computing framework for the detection of epileptic seizures, comparing different feature approaches mapped to HD vectors. More precisely, we test two previously implemented features as well as several novel approaches with HD computing on epileptic seizure detection. We evaluate them in a comparable way, i.e., with the same preprocessing setup, and with the identical performance measures. We use two different datasets in order to assess the generalizability of our conclusions. The systematic assessment involved three primary aspects relevant for potential wearable implementations: 1) detection performance, 2) memory requirements, and 3) computational complexity. Our analysis shows a significant difference in detection performance between approaches, but also that the ones with the highest performance might not be ideal for wearable applications due to their high memory or computational requirements. Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.