ArticleCognitive neurodynamics2015
Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables.
Article in Cognitive neurodynamics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02938325 (Monitoring Awareness and Pain Under Anesthesia Using a New EEG Based System), which is not on this map. Cited by 21 papers.
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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.
Monitoring Awareness and Pain Under Anesthesia Using a New EEG Based System
Who cites it
21 citing papers in PubMed.
- Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness.PloS one · 2021Trial
- The discriminatory value of cardiorespiratory interactions in distinguishing awake from anaesthetised states: a randomised observational study.Anaesthesia · 2015Trial
- Association Between Entropy Monitoring, Burst Suppression and Early Postoperative Cognitive Dysfunction in Emergency Surgery: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Anesthesia depth prediction from drug infusion history using hybrid AI.BMC medical informatics and decision making · 2025Article
- Article
- Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.Journal of cardiothoracic and vascular anesthesia · 2024Review
- Signal processing and machine learning algorithm to classify anaesthesia depth.BMJ health & care informatics · 2023Observational
- Dysfunction of the Glymphatic System as a Potential Mechanism of Perioperative Neurocognitive Disorders.Frontiers in aging neuroscience · 2021Review
- Monitoring Depth of Anesthesia Based on Hybrid Features and Recurrent Neural Network.Frontiers in neuroscience · 2020Article
- Analyzing Electroencephalography (EEG) Waves Provides a Reliable Tool to Assess the Depth of Sevoflurane Anesthesia in Pediatric Patients.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model.Cognitive neurodynamics · 2018Article
- Measures of entropy and complexity in altered states of consciousness.Cognitive neurodynamics · 2018Article
- Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression.Frontiers in pharmacology · 2018Article
- Evaluation of prolonged administration of isoflurane on cerebral blood flow and default mode network in macaque monkeys anesthetized with different maintenance doses.Neuroscience letters · 2018Article
- Article
- An EEG-based machine learning method to screen alcohol use disorder.Cognitive neurodynamics · 2017Article
- Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives.Korean journal of anesthesiology · 2016Review
- Aesthetic preference recognition of 3D shapes using EEG.Cognitive neurodynamics · 2016Article
- Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method.Journal of medical systems · 2016Article
- Power spectral density and coherence analysis of Alzheimer's EEG.Cognitive neurodynamics · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.
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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.