Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection

Ekaterina Kutafina; Alexander Brenner; Yannic Titgemeyer; Rainer Surges; Stephan Jonas

doi:10.7717/peerj.8969

Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection

PeerJ. 2020 May 1:8:e8969. doi: 10.7717/peerj.8969. eCollection 2020.

Authors

Ekaterina Kutafina^{1

2}, Alexander Brenner¹, Yannic Titgemeyer¹, Rainer Surges³, Stephan Jonas⁴

Affiliations

¹ Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.
² Faculty of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland.
³ Department of Epileptology, University Hospital of Bonn, Bonn, Germany.
⁴ Department of Informatics, Technical University of Munich, Garching, Germany.

Abstract

Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 min long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.

Keywords: EEG; Epilepsy; Mobile health; Simultaneous recording; Time series; Wearable sensor.

Grants and funding

This work was supported by the Faculty of Applied Mathematics AGH UST statutory tasks within subsidy of Ministry of Science and Higher Education (Ekaterina Kutafina). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.