TY - JOUR AU - Zhan, Jian AU - Wu, Zhuo-xi AU - Duan, Zhen-xin AU - Yang, Gui-ying AU - Du, Zhi-yong AU - Bao, Xiao-hang AU - Li, Hong PY - 2021 DA - 2021/03/02 TI - Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states JO - BMC Anesthesiology SP - 66 VL - 21 IS - 1 AB - Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment. SN - 1471-2253 UR - https://doi.org/10.1186/s12871-021-01285-x DO - 10.1186/s12871-021-01285-x ID - Zhan2021 ER -