A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement
- PDF / 1,645,851 Bytes
- 19 Pages / 439.37 x 666.142 pts Page_size
- 90 Downloads / 187 Views
A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement Shasha Zhang1 · Dan Chen1 · Rajiv Ranjan2,3 · Hengjin Ke1 · Yunbo Tang1 · Albert Y. Zomaya4 Accepted: 31 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract It is critical to determine whether the brain state of an epilepsy patient is indicative of a possible seizure onset; thus, appropriate therapy or alarm may be delivered in time. Successful seizure prediction relies on the capability of accurately separating the preictal stage from the interictal stage of ictal electroencephalography (EEG). With the booming of brain e-health technologies, there exists a pressing need for an approach that provides accurate seizure prediction while operating efficiently on edge computing platforms with very limited computing resources in Internet of Things environments. This study proposes a lightweight solution to this problem based on synchronization measurement of multivariate EEG captured from multiple brain regions consisting of two phases, i.e., synchronization measurement and classification. For phase one, Pearson correlation coefficient is calculated to obtain the correlation matrices. For phase two, the correlation matrices are classified to distinguish the preictal states from the interictal ones with a simple CNN model, and seizure onset can then be predicted. Experiments have been performed to evaluate the performance of the lightweight solution on the CHB-MIT scalp EEG dataset. The experimental results indicate that: (1) the solution outperforms most of the state-ofthe-art counterparts with a high accuracy of seizure prediction ( 89.98% for 15 mins alarm in advance) for all subjects, and (2) the solution incurs a very low computational overhead and holds potentials in brain e-health applications. Keywords Seizure prediction · Synchronization measurement · Brain e-health · EEG · Epilepsy
* Dan Chen [email protected] Extended author information available on the last page of the article
13
Vol.:(0123456789)
S. Zhang et al.
1 Introduction Epilepsy has been affecting more than 50 million people around the world [39]. Repeatedly emergent seizures accompanied by the disordered neuronal activities can cause server damages to the patients [27]. It is critical to determine whether the brain state of an epilepsy patient is indicative of a possible seizure onset; thus, appropriate therapy or alarm may be delivered in time. This has long been an onerous and challenging task in epilepsy research and practice. Successful seizure prediction largely relies on the capability of accurately separating the preictal stage (before seizure) from the interictal stage (between seizures) of ictal EEG. This aims to benefit from the high temporal resolution of scalp EEG and its dominance in neuroscience community with significant successes achieved in the study of epileptic seizures from detection [6] to prediction. Traditional signal processing technologies have been widely applied for this purpose.
Data Loading...
