Depth classification algorithm of anesthesia based on model fusion
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18727-6.pdf
Reference47 articles.
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3. Myles PS, Leslie K, McNeil J et al (2004) Bispectral index monitoring to prevent awareness during anaesthesia: the B-Aware randomised controlled trial. Lancet 363(9423):1757–1763. https://doi.org/10.1016/S0140-6736(04)16300-9
4. Xu L, Wu AS, Yue Y (2009) The incidence of intra-operative awareness during general anesthesia in China: a multi-center observational study. Acta Anaesthesiol Scand 53(7):873–882. https://doi.org/10.1111/j.1399-6576.2009.02016.x
5. Al-Kadi MI, Reaz MB, Ali MAM (2013) Evolution of electroencephalogram signal analysis techniques during anesthesia. Sensors 13(5):6605–6635. https://doi.org/10.3390/s130506605
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1. XAI-VSDoA: An Explainable AI-Based Scheme Using Vital Signs to Assess Depth of Anesthesia;IEEE Access;2024
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