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 …
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 …
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
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 …
(DOA) because EEG reflects surgical pain and state of the brain. However, precise and real …
Proposed EEG measures of consciousness: a systematic, comparative review.
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 …
improving clinical diagnostics and therapy as well as for investigating consciousness per se …
Memory requirements for convolutional neural network hardware accelerators
The rapid pace and successful application of machine learning research and development
has seen widespread deployment of deep convolutional neural networks (CNNs). Alongside …
has seen widespread deployment of deep convolutional neural networks (CNNs). Alongside …
Monitoring the depth of anesthesia using a new adaptive neurofuzzy system
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 …
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
Estimating the depth of anaesthesia (DoA) in operations has always been a challenging
issue due to the underlying complexity of the brain mechanisms. Electroencephalogram …
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 …
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 …
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
Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical
settings and neuroscience investigations to understand brain mechanisms …
settings and neuroscience investigations to understand brain mechanisms …