Path Signatures for Seizure Forecasting

TL;DR

This paper explores using path signatures to forecast epileptic seizures from EEG data, demonstrating a simple, customizable approach with performance comparable to modern machine learning, but highlighting limitations due to brain data complexity.

Contribution

It introduces the application of path signatures for seizure prediction, providing a theoretically grounded, yet practically comparable, method to existing machine learning techniques.

Findings

Path signature method achieves forecasting performance similar to modern machine learning.

EEG data alone may be insufficient for reliable seizure prediction due to brain complexity.

The approach offers a simple, customizable pattern recognition pipeline.

Abstract

Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epileptic seizures from brain activity measurements, using EEG data, remains largely unresolved despite much dedicated research effort. Based on a longitudinal and state-of-the-art data set using intercranial EEG measurements from people with epilepsy, we consider the automated discovery of predictive features (or biomarkers) to forecast seizures in a patient-specific way. To this end, we use the path signature, a recent development in the analysis of data streams, to map from measured time series to seizure prediction. The predictor is based on linear classification, here augmented with sparsity constraints, to discern time series with and without an impending seizure. This approach may be seen as a step towards a generic pattern recognition pipeline where the main advantages are simplicity and ease of customisation, while maintaining forecasting performance on par with modern machine learning. Nevertheless, it turns out that although the path signature method has some powerful theoretical guarantees, appropriate time series statistics can achieve essentially the same results in our context of seizure prediction. This suggests that, due to their inherent complexity and non-stationarity, the brain's dynamics are not identifiable from the available EEG measurement data, and, more concretely, epileptic episode prediction is not reliably achieved using EEG measurement data alone.