ArticleHealth information science and systems2022
A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia.
Article in Health information science and systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Raw EEG as a viable alternative to engineered decompositions in anesthetic depth prediction.Journal of anesthesia · 2026Article
- Predicting depth of anaesthesia from single-channel EEG using a deep TCN-BiLSTM-attention model with EWMA.Scientific reports · 2026Article
- Divergent periodic and aperiodic EEG signatures of Propofol versus sevoflurane anesthesia: a comparative neurophysiological study.Frontiers in medicine · 2026Article
- Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants.Journal of clinical monitoring and computing · 2025Article
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
- Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features.Brain informatics · 2024Article
- Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.Frontiers in neuroscience · 2024Article
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
The requirement for anaesthesia during modern surgical procedures is unquestionable to ensure a safe experience for patients with successful recovery. Assessment of the depth of anaesthesia (DoA) is an important and ongoing field of research to ensure patient stability during and post-surgery. This research addresses the limitations of current DoA indexes by developing a new index based on electroencephalography (EEG) signal analysis. Empirical wavelet transformation (EWT) methods are employed to extract wavelet coefficients before statistical analysis. The features Spectral Entropy and Second Order Difference Plot are extracted from the wavelet coefficients. These features are used to train a new index, SSE
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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.