<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Processing of signals acquired from sensor systems needs accurate algorithms to extract information of interest concerning the problem under study. In this work Empirical Mode Decomposition method is used on EEG signals obtained during polysomnography examination, when electromyographic (EMG) signals are acquired too. EMD method decomposes a signal into components named Intrinsic Mode Functions (IMF) which can exhibit important time-frequency information related to signals under observation. Since EEG signals are obtained from multiple electrodes, the problem is addressed to processing of signals acquired from multiple channels according to sensor array techniques. The objective of this work is to define an automatic method to detect transient event, changes of sleep stage in EEG signals which can allow an evaluation of the state of the patient. In this first step we analyze and process EEG signals through EMD method to remove EMG contributes. The obtained results are encouraging in the definition of a future multivariate approach to quantify brain activity by evaluating correlation of the IMFs calculated for each channel.</p></abstract>

Processing of signals acquired from sensor systems needs accurate algorithms to extract information of interest concerning the problem under study. In this work Empirical Mode Decomposition method is used on EEG signals obtained during polysomnography examination, when electromyographic (EMG) signals are acquired too. EMD method decomposes a signal into components named Intrinsic Mode Functions (IMF) which can exhibit important time-frequency information related to signals under observation. Since EEG signals are obtained from multiple electrodes, the problem is addressed to processing of signals acquired from multiple channels according to sensor array techniques. The objective of this work is to define an automatic method to detect transient event, changes of sleep stage in EEG signals which can allow an evaluation of the state of the patient. In this first step we analyze and process EEG signals through EMD method to remove EMG contributes. The obtained results are encouraging in the definition of a future multivariate approach to quantify brain activity by evaluating correlation of the IMFs calculated for each channel.

Analyzing and Processing EEG-based Multichannel Signals Acquired during Sleeping

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Analyzing and Processing EEG-based Multichannel Signals Acquired during Sleeping"}

Processing of signals acquired from sensor systems needs accurate algorithms to extract information of interest concerning the problem under study. In this...

Analyzing and Processing EEG-based Multichannel Signals Acquired during Sleeping

Published Online: Feb 15, 2020

Page range: 1 - 4

DOI: https://doi.org/10.21307/ijssis-2019-125

Keywords
component, Biomedical measurements, signal processing, sleep EEG, Empirical Mode Decomposition

© 2019 Authors., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Analyzing and Processing EEG-based Multichannel Signals Acquired during Sleeping

Published Online: Feb 15, 2020

Page range: 1 - 4

DOI: https://doi.org/10.21307/ijssis-2019-125

Keywordscomponent, Biomedical measurements, signal processing, sleep EEG, Empirical Mode Decomposition

© 2019 Authors., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
component, Biomedical measurements, signal processing, sleep EEG, Empirical Mode Decomposition