Observational studyBMJ health & care informatics2023
Signal processing and machine learning algorithm to classify anaesthesia depth.
Observational study in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- A Multitask Time-Frequency Deep Learning Approach for Anesthesia Depth Monitoring and Transition Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
- Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.PloS one · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPoor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures.
methodsObservational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1.
resultsA total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states.
conclusionBiological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.
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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.