Artificial intelligence and machine learning in anesthesiology

CW Connor - Anesthesiology, 2019 - pubs.asahq.org
Commercial applications of artificial intelligence and machine learning have made
remarkable progress recently, particularly in areas such as image recognition, natural …

Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines

T Lajnef, S Chaibi, P Ruby, PE Aguera… - Journal of neuroscience …, 2015 - Elsevier
Background Sleep staging is a critical step in a range of electrophysiological signal
processing pipelines used in clinical routine as well as in sleep research. Although the …

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

S Afshar, R Boostani, S Sanei - IEEE Journal of Biomedical and …, 2021 - ieeexplore.ieee.org
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 …

Proposed EEG measures of consciousness: A systematic, comparative review.

AS Nilsen, B Juel, B Thürer, JF Storm - 2020 - osf.io
Abstract Knowledge of which brain properties are required for consciousness is essential for
improving clinical diagnostics and therapy as well as for investigating consciousness per se …

Memory requirements for convolutional neural network hardware accelerators

K Siu, DM Stuart, M Mahmoud… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
The rapid pace and successful application of machine learning research and development
has seen widespread deployment of deep convolutional neural networks (CNNs). Alongside …

Monitoring the depth of anesthesia using a new adaptive neurofuzzy system

A Shalbaf, M Saffar, JW Sleigh… - IEEE journal of …, 2017 - ieeexplore.ieee.org
Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable.
Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain …

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Q Liu, L Ma, SZ Fan, MF Abbod, JS Shieh - PeerJ, 2018 - peerj.com
Estimating the depth of anaesthesia (DoA) in operations has always been a challenging
issue due to the underlying complexity of the brain mechanisms. Electroencephalogram …

Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network

MA Mohammed, MK Abd Ghani, N Arunkumar… - The Journal of …, 2020 - Springer
The segregation among benign and malignant nasopharyngeal carcinoma (NPC) from
endoscopic images is one of the most challenging issues in cancer diagnosis because of …

Use of multiple EEG features and artificial neural network to monitor the depth of anesthesia

Y Gu, Z Liang, S Hagihira - Sensors, 2019 - mdpi.com
The electroencephalogram (EEG) can reflect brain activity and contains abundant
information of different anesthetic states of the brain. It has been widely used for monitoring …

Inference of brain states under anesthesia with meta learning based deep learning models

Q Wang, F Liu, G Wan, Y Chen - IEEE transactions on neural …, 2022 - ieeexplore.ieee.org
Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical
settings and neuroscience investigations to understand brain mechanisms …