University of Oulu

N. Bahador and J. Kortelainen, "A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2503-2510, 2021, doi: 10.1109/TNSRE.2021.3128620

A robust bimodal index reflecting relative dynamics of EEG and HRV with application in monitoring depth of anesthesia

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Author: Bahador, Nooshin1; Kortelainen, Jukka1
Organizations: 1Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, 90570 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-01-28


Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.

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Series: IEEE transactions on neural systems and rehabilitation engineering
ISSN: 1534-4320
ISSN-E: 1558-0210
ISSN-L: 1534-4320
Volume: 29
Pages: 2503 - 2510
DOI: 10.1109/TNSRE.2021.3128620
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland and Infotech under Grant 308935. The work of Nooshin Bahador was supported in part by the Orion Research Foundation sr, in part by the Walter Ahlström Foundations, in part by the Tauno Tönningin Säätiö, and in part by Nokia Foundation.
Academy of Finland Grant Number: 308935
Detailed Information: 308935 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see