A Real-Time ECG Feature Extraction Algorithm for Detecting Meditation Levels within a General Measurement Setup

This paper presents a setup for the real-time extraction of Electroencephalography (EEG) and Electrocardiogram (ECG) features indicating the level of focus, relaxation, or meditation of a given subject. An algorithm for detecting meditation in real-time using the extracted ECG features is designed a...

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Bibliographic Details
Published inConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2019; pp. 99 - 103
Main Authors Alawieh, Hussein, Dawy, Zaher, Yaacoub, Elias, Abbas, Nabil, El-Imad, Jamil
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Algorithms
Biomedical monitoring
Electrocardiography
Electroencephalography
Feature extraction
Heart rate variability
Meditation
Neurofeedback
Real-time systems
Online AccessGet full text
ISSN1557-170X
1558-4615
DOI10.1109/EMBC.2019.8857832

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Summary:This paper presents a setup for the real-time extraction of Electroencephalography (EEG) and Electrocardiogram (ECG) features indicating the level of focus, relaxation, or meditation of a given subject. An algorithm for detecting meditation in real-time using the extracted ECG features is designed and shown to lead to accurate results using an online ECG measurement dataset. Similar methods can be used for EEG data, such that the proposed measurement setup can be used, for example, for investigating the effect of virtual reality based EEG training, with and without neurofeedback, on the capability of subjects to focus, relax, or meditate.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8857832