RT Journal Article
SR Electronic
T1 EEG-Beats: Automated analysis of heart rate variability (HVR) from EEG-EKG
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.21.211862
DO 10.1101/2020.07.21.211862
A1 Thanapaisal, Supakjeera
A1 Mosher, Sabrina
A1 Trejo, Brenda
A1 Robbins, Kay
YR 2020
UL http://biorxiv.org/content/early/2020/07/22/2020.07.21.211862.abstract
AB Heart rate variability (HRV), the variation of the period between consecutive heartbeats, is an established tool for assessing physiological indicators such as stress and fatigue. In non-clinical settings, HRV is often computed from signals acquired using wearable devices that are susceptible to strong artifacts. In EEG (electroencephalography) experiments, these devices must be synchronized with the EEG and typically provide intermittent interbeat interval information based on proprietary artifact-removal algorithms. This paper describes an automated algorithm that uses the output of an EEG sensor mounted on a subject’s chest to accurately detect interbeat intervals and to calculate time-varying metrics. The algorithm is designed for raw signals and is robust to artifacts, resulting in fine-grained capture of HRV that is synchronized with the EEG. An open-source MATLAB toolbox (EEG-Beats) is available to calculate interbeat intervals and many standard HRV time and frequency indicators. EEG-Beats is designed to run in a completely automated fashion on an entire study without manual intervention. The paper applies EEG-Beats to EKG signals measured with an EEG sensor in a large longitudinal study (17 subjects, 6 tasks, 854 datasets). The toolbox is available at https://github.com/VisLab/EEG-Beats.Competing Interest StatementThe authors have declared no competing interest.