Trial reportSensors (Basel, Switzerland)2019
Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.
Trial report in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and its clinical application in Anesthesiology: a systematic review.Journal of clinical monitoring and computing · 2024Pooled it
- Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks.Sensors (Basel, Switzerland) · 2026Article
- Depth of anesthesia monitoring: Current evidence, clinical impact, and future directions.The Journal of international medical research · 2026Review
- Clinical Application of Artificial Intelligence in Anesthesiology: A Multicenter Retrospective Comparison Between Human Anesthetic Decisions and Algorithmic Recommendations in Non-Cardiac Surgery.Journal of personalized medicine · 2026Article
- Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study.JMIR medical informatics · 2026Article
- Advancing a Hybrid Decision-Making Model in Anesthesiology: Applications of Artificial Intelligence in the Perioperative Setting.Healthcare (Basel, Switzerland) · 2025Review
- Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants.Journal of clinical monitoring and computing · 2025Article
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Neural models for detection and classification of brain states and transitions.Communications biology · 2025Article
- Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.Neuroscience bulletin · 2025Article
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
- Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.PloS one · 2025Article
- Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.Journal of cardiothoracic and vascular anesthesia · 2024Review
- Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors.Frontiers in medicine · 2024Review
- Unconscious classification of quantitative electroencephalogram features from propofol versus propofol combined with etomidate anesthesia using one-dimensional convolutional neural network.Frontiers in medicine · 2024Article
- Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach.Biomedicines · 2023Article
- Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security.Medical science monitor : international medical journal of experimental and clinical research · 2023Review
- Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network.Sensors (Basel, Switzerland) · 2023Article
- A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia.Health information science and systems · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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 depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( p < 0.001 ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.