AIMC Topic: Intraoperative Neurophysiological Monitoring
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Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.
International journal of neural systems
Apr 16, 2025
Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated with adverse outcomes such as postoperative delirium, extended recovery time, and increased postope...
Paradigms of intraoperative neuromonitoring in paediatric thyroid surgery.
Frontiers in endocrinology
Jan 30, 2025
The larynx of children and adolescents is still in the developmental phase and the anatomical structure is still very small and sensitive. The higher malignancy and faster progression of some paediatric thyroid cancers make surgery more difficult. In...
Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.
Annals of vascular surgery
Nov 22, 2024
BACKGROUND: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered ...
Neurologic Examination
Endarterectomy, Carotid
Treatment Outcome
Predictive Value of Tests
Wakefulness
Carotid Stenosis
Risk Factors
Aged
Middle Aged
Decision Support Techniques
Humans
Aged, 80 and over
Retrospective Studies
Risk Assessment
Machine Learning
Male
Female
Case-Control Studies
Reproducibility of Results
Intraoperative Neurophysiological Monitoring
Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.
Neuroscience bulletin
Sep 17, 2024
General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators...
Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Aug 24, 2024
OBJECTIVE: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgr...
Machine learning allows expert level classification of intraoperative motor evoked potentials during neurosurgical procedures.
Computers in biology and medicine
Aug 21, 2024
OBJECTIVE: To develop and evaluate machine learning (ML) approaches for muscle identification using intraoperative motor evoked potentials (MEPs), and to compare their performance to human experts.
Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network.
Computational and mathematical methods in medicine
Jan 29, 2022
METHODS: We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure c...
A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.
IEEE transactions on biomedical circuits and systems
May 28, 2020
In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset...
Monitoring the level of hypnosis using a hierarchical SVM system.
Journal of clinical monitoring and computing
Apr 15, 2019
Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the lev...
Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach.
Journal of neural engineering
Jan 31, 2019
OBJECTIVE: Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. ...
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