A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation from EEG Signals

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...

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Bibliographic Details
Main Authors:	Afshar, S. (Author), Boostani, R. (Author), Sanei, S. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	adult algorithm Algorithms anesthesia Anesthesia anesthesia level Anesthesiology Area under the curves Article artificial neural network attention layer Attention mechanism awareness bispectral index Conventional methods convolutional neural network Convolutional neural networks deep learning Deep learning Deep Learning depth of anesthesia Direction of arrival DOA estimation method electroencephalogram electroencephalography Electroencephalography Electrophysiology entropy evaluation metrics female functional connectivity general anesthesia human Humans Imbalance distributions intensive care unit Large dataset learning algorithm long short term memory network long short-term memory machine learning major clinical study male mathematical model Mean square error Multilayer neural networks nerve cell network Neural Networks, Computer postoperative complication Postoperative complications proposed method Real-time estimation Root mean square errors sedation sensitivity and specificity signal noise ratio spinal anesthesia Subject classification support vector machine Surgery time perception training validation process
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A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation from EEG Signals

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