AnesNet: A Versatile Deep Convolutional Framework for Predicting Depth of Anesthesia Using BIS and Multimodal Intraoperative Signals

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@article{24ba24c4000945ac9f3a5b745eb0d256,

title = "AnesNet: A Versatile Deep Convolutional Framework for Predicting Depth of Anesthesia Using BIS and Multimodal Intraoperative Signals",

author = "Yunjia Qi and Xiaoxiao Wang and Yichen Cui and Kaixi Liu and Luhua Chen and Xiangyang Guo and Zhengqian Li and Sichen Liu",

year = "2026",

month = feb,

doi = "10.1186/s12871-026-03710-5",

language = "English",

journal = "BMC Anesthesiology",

issn = "1471-2253",

}

TY - JOUR

T1 - AnesNet: A Versatile Deep Convolutional Framework for Predicting Depth of Anesthesia Using BIS and Multimodal Intraoperative Signals

AU - Qi, Yunjia

AU - Wang, Xiaoxiao

AU - Cui, Yichen

AU - Liu, Kaixi

AU - Chen, Luhua

AU - Guo, Xiangyang

AU - Li, Zhengqian

AU - Liu, Sichen

PY - 2026/2

Y1 - 2026/2

U2 - 10.1186/s12871-026-03710-5

DO - 10.1186/s12871-026-03710-5

M3 - Article

SN - 1471-2253

JO - BMC Anesthesiology

JF - BMC Anesthesiology

ER -