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

Hussein Alawieh; Zaher Dawy; Elias Yaacoub; Nabil Abbas; Jamil El-Imad

doi:10.1109/EMBC.2019.8857832

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

Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul:2019:99-103. doi: 10.1109/EMBC.2019.8857832.

Authors

Hussein Alawieh, Zaher Dawy, Elias Yaacoub, Nabil Abbas, Jamil El-Imad

PMID: 31945854
DOI: 10.1109/EMBC.2019.8857832

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electrocardiography
Electroencephalography
Meditation*
Neurofeedback*