ArticleFrontiers in neuroscience2020
Monitoring Depth of Anesthesia Based on Hybrid Features and Recurrent Neural Network.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
11 citing papers in PubMed.
- Effect of nalbuphine and morphine as adjuvants to bupivacaine in ultrasound-guided supraclavicular block: a randomized controlled trial with machine learning-based predictive analysis.BMC anesthesiology · 2025Trial
- Value of narcotrend anesthesia depth monitoring in predicting POCD in gastrointestinal tumor anesthesia block patients.BMC anesthesiology · 2024Trial
- Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks.Sensors (Basel, Switzerland) · 2026Article
- Age-Dependent Entropic Features During Propofol Anesthesia in Developing Brain.Anesthesia and analgesia · 2026Article
- Prognostic value of cardiac magnetic resonance-derived myocardial segmental motion curve complexity metrics for recovery ability in post-myocardial infarction patients.Quantitative imaging in medicine and surgery · 2025Article
- Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.PloS one · 2025Article
- Article
- Developing a robust model to predict depth of anesthesia from single channel EEG signal.Physical and engineering sciences in medicine · 2022Article
- Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review.Computational intelligence and neuroscience · 2022Review
- Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth.Frontiers in neuroinformatics · 2022Article
- Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study.Frontiers in human neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalogram (EEG) signals contain valuable information about the different physiological states of the brain, with a variety of linear and nonlinear features that can be used to investigate brain activity. Monitoring the depth of anesthesia (DoA) with EEG is an ongoing challenge in anesthesia research. In this paper, we propose a novel method based on Long Short-Term Memory (LSTM) and a sparse denoising autoencoder (SDAE) to combine the hybrid features of EEG to monitor the DoA. The EEG signals were preprocessed using filtering, etc., and then more than ten features including sample entropy, permutation entropy, spectra, and alpha-ratio were extracted from the EEG signal. We then integrated the optional features such as permutation entropy and alpha-ratio to extract the essential structure and learn the most efficient temporal model for monitoring the DoA. Compared with using a single feature, the proposed model could accurately estimate the depth of anesthesia with higher prediction probability (
Indexed as
Identifiers
What OpenQuestion holds
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.