A combinatorial deep learning structure for precise depth of anesthesia estimation from EEG signals

Afshar, S, Boostani, R and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2021. A combinatorial deep learning structure for precise depth of anesthesia estimation from EEG signals. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194

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Abstract

Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. However, precise and real-time estimation of DOA index for painful surgical operations is challenging due to problems such as postoperative complications and accidental awareness. To tackle these problems, we propose a new combinatorial deep learning structure involving convolutional neural networks (inspired by the inception module), bidirectional long short-term memory, and an attention layer. The proposed model uses the EEG signal to continuously predicts the bispectral index (BIS). It is trained over a large dataset, mostly from those under general anesthesia with few cases receiving sedation/analgesia and spinal anesthesia. Despite the imbalance distribution of BIS values in different levels of anesthesia, our proposed structure achieves convincing root mean square error of 5.59 ± 1.04 and mean absolute error of 4.3 ± 0.87, as well as improvement in area under the curve of 15% on average, which surpasses state-of-the-art DOA estimation methods. The DOA values are also discretized into four levels of anesthesia and the results demonstrate strong inter-subject classification accuracy of 88.7% that outperforms the conventional methods.

Item Type:	Journal article
Publication Title:	IEEE Journal of Biomedical and Health Informatics
Creators:	Afshar, S., Boostani, R. and Sanei, S.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	25 March 2021
ISSN:	2168-2194
Identifiers:	Number Type 10.1109/jbhi.2021.3068481 DOI 33760743 PubMed ID 1428522 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	07 Apr 2021 16:04
Last Modified:	31 May 2021 15:04
URI:	https://irep.ntu.ac.uk/id/eprint/42662

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