Citation

Xu Y, Yang J, Sawan M. J. Signal Process. Syst. 2022; 94(6): 527-542.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11265-021-01659-x

PMID

unavailable

Abstract

The rapid contemporary development of wearable devices offers non-invasive and effective approaches for monitoring the human brain. Recent studies have investigated the prediction of epileptic seizures (ESs) using wearable measurements, such as scalp electroencephalography and functional near-infrared spectroscopy. The signal processing tasks are the core component of emerging closed-loop ES prediction (ESP) systems. Various research groups have introduced many state-of-the-art signal processing techniques to improve ESP performance. Wearable measurements consider low frequency and low spatial resolution characteristics. In this paper, we provide a comprehensive review of signal processing techniques including preprocessing, feature extraction, dimensionality reduction and classification schemes for ESP systems. Trends and concerns of ESP studies at the end of the manuscript.

Language: en

Keywords

Classification; Deep learning; Epilepsy; Feature extraction; Machine learning; Prediction trends; Seizure; Signal processing