Trial reportBioMed research international2015
Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.
Trial report in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed, 32 citations in OpenAlex.
- Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness.PloS one · 2021Trial
- Assessing the use of the Index of Consciousness (IoC) as a monitoring tool for the sedative effects of ciprofol during general anesthesia induction: a prospective observational study.Perioperative medicine (London, England) · 2025Article
- Perception and practices of depth of anesthesia monitoring and intraoperative awareness event rate among Jordanian anesthesiologists: a cross-sectional study.BMC anesthesiology · 2022Article
- Comparison of Deep Learning Algorithms in Predicting Expert Assessments of Pain Scores during Surgical Operations Using Analgesia Nociception Index.Sensors (Basel, Switzerland) · 2022Article
- Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth.Frontiers in neuroinformatics · 2022Article
- Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review.Computational intelligence and neuroscience · 2022Review
- A survey of current practices, attitudes and demands of anaesthesiologists regarding the depth of anaesthesia monitoring in China.BMC anesthesiology · 2021Article
- Quantitative assessment of consciousness during anesthesia without EEG data.Journal of clinical monitoring and computing · 2021Article
- Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder.Sensors (Basel, Switzerland) · 2021Article
- [Machine learning in anesthesiology].Der Anaesthesist · 2020Review
- Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.Sensors (Basel, Switzerland) · 2020Article
- Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia withSensors (Basel, Switzerland) · 2019Article
- Article
- Control Law Design for Propofol Infusion to Regulate Depth of Hypnosis: A Nonlinear Control Strategy.Computational and mathematical methods in medicine · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.
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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.