Maintaining anesthetic states using automated brain-state prediction systems is expected to reduce drug overdosage and associated side-effects. However, commercially available brain-state monitoring systems perform poorly on drug-class combinations. We assume that current automated brain-state prediction systems perform poorly because they do not account for brain-state dynamics that are unique to drug-class combinations. In this work, we develop a k-nearest neighbors model to test whether improvements to automated brain-state prediction of drug-class combinations are feasible. We utilize electroencephalogram data collected from human subjects who received general anesthesia with sevoflurane and general anesthesia with the drug-class combination of sevoflurane-plus-ketamine. We demonstrate improved performance predicting anesthesia-induced brain-states using drug-specific models.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077760 | PMC |
| http://dx.doi.org/10.1109/EMBC.2019.8856935 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Novel Real-time Phase Prediction Network in EEG Rhythm.
Neurosci Bull
November 2024
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data.
View Article and Find Full Text PDFAutomated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy.
Ann Clin Transl Neurol
December 2024
Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
Objective: Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy.
Methods: Trends of BSN, a deep learning-based measure translating EEG background to a continuous trend, were studied from a three-channel montage long-term EEG monitoring from a prospective cohort of 92 infants with neonatal encephalopathy and neurodevelopmental outcomes assessed by Bayley Scales of Infant Development, 3rd edition (Bayley-III) at 18 months. Outcome prediction used categories "Severe impairment" (Bayley-III composite score ≤70 or death) or "Any impairment" (score ≤85 or death).
Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework.
Biomed Phys Eng Express
October 2024
R&D, Neurocare Group AG, Munich, Germany.
Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity.
View Article and Find Full Text PDFAn automated sleep staging tool based on simple statistical features of mice electroencephalography (EEG) and electromyography (EMG) data.
Eur J Neurosci
October 2024
Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan.
Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep-wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG.
View Article and Find Full Text PDFDecoding the Preparation Stage of Target Shooting under Audiovisual Restricted Conditions: Investigating Neural Mechanisms Using Microstate Analysis.
Brain Topogr
November 2024
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650000, China.
Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!