ArticleJournal of clinical monitoring and computing2020
Monitoring the level of hypnosis using a hierarchical SVM system.
Article in Journal of clinical monitoring and computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Artificial intelligence and its clinical application in Anesthesiology: a systematic review.Journal of clinical monitoring and computing · 2024Pooled it
- Hierarchical Poincaré analysis for anaesthesia monitoring.Journal of clinical monitoring and computing · 2020Trial
- Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.Sensors (Basel, Switzerland) · 2019Trial
- Age-Dependent Entropic Features During Propofol Anesthesia in Developing Brain.Anesthesia and analgesia · 2026Article
- An EEG-based marker of functional connectivity: detection of major depressive disorder.Cognitive neurodynamics · 2024Article
- Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra.Journal of clinical monitoring and computing · 2024Article
- An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals.Physical and engineering sciences in medicine · 2023Article
- Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.Cognitive neurodynamics · 2022Article
- Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals.Journal of biomedical physics & engineering · 2022Article
- Effect of Comprehensive Rehabilitation Training Program in Orthopedic Nursing of Patients with Residual Limb Injury Caused by Crush.Journal of healthcare engineering · 2022Article
- Boosting framework via clinical monitoring data to predict the depth of anesthesia.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.Cognitive neurodynamics · 2021Article
- Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.BMC anesthesiology · 2021Article
- Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches.Journal of clinical medicine · 2020Article
- Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia.Cognitive neurodynamics · 2019Article
- Graph-based Analysis to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Patients With Major Depressive Disorder Using EEG Signals.Basic and clinical neuroscienceArticle
- A Hybrid EEG-based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks and Support Vector Machine.Basic and clinical neuroscienceArticle
- Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals.Basic and clinical neuroscienceArticle
- Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.Basic and clinical neuroscienceArticle
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).
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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.