ArticleJournal of medical systems2016
Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Intelligent Rehabilitation: Advances in Artificial Intelligence for Musculoskeletal Rehabilitation: A Narrative Review.Orthopedic research and reviews · 2026Review
- The Role of Artificial Intelligence in Anesthesia Monitoring and Surveillance.Anesthesiology clinics · 2025Review
- Harnessing Artificial Intelligence (AI) in Anaesthesiology: Enhancing Patient Outcomes and Clinical Efficiency.Cureus · 2024Review
- Artificial Intelligence in Perioperative Planning and Management of Liver Resection.Indian journal of surgical oncology · 2024Review
- Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors.Frontiers in medicine · 2024Review
- Role of artificial intelligence in perioperative monitoring in anaesthesia.Indian journal of anaesthesia · 2024Article
- Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis.Entropy (Basel, Switzerland) · 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
- Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network.Heliyon · 2023Article
- Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery.Anesthesiology clinics · 2021Review
- Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.PloS one · 2021Article
- Monitoring the level of hypnosis using a hierarchical SVM system.Journal of clinical monitoring and computing · 2020Article
- Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations.Anesthesiology · 2020Review
- Surgical data science: The new knowledge domain.Innovative surgical sciences · 2017Article
- Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs.Computational intelligence and neuroscience · 2017Article
- Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives.Korean journal of anesthesiology · 2016Review
- Spectral Gini Index for Quantifying the Depth of Consciousness.Computational intelligence and neuroscience · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.
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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.