Developing a robust model to predict depth of anesthesia from single channel EEG signal

Article


Alsafy, Iman and Diykh, Mohammed. 2022. "Developing a robust model to predict depth of anesthesia from single channel EEG signal." Physical and Engineering Sciences in Medicine. 45 (3), pp. 793-808. https://doi.org/10.1007/s13246-022-01145-z
Article Title

Developing a robust model to predict depth of anesthesia from single channel EEG signal

ERA Journal ID5034
Article CategoryArticle
AuthorsAlsafy, Iman and Diykh, Mohammed
Journal TitlePhysical and Engineering Sciences in Medicine
Journal Citation45 (3), pp. 793-808
Number of Pages16
Year2022
PublisherSpringer
Place of PublicationNetherlands
ISSN0158-9938
1879-5447
Digital Object Identifier (DOI)https://doi.org/10.1007/s13246-022-01145-z
Web Address (URL)https://link.springer.com/article/10.1007/s13246-022-01145-z
Abstract

Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient’s state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.

KeywordsSliding window ; Statistical metrics; BIS ; HDE ; EEG ; DoA ; CGDA
Byline AffiliationsUniversity of Thi-Qar, Iraq
UniSQ College
Al-Ayen University, Iraq
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