Liu Quan, Ma Li, Chiu Ren-Chun, Fan Shou-Zen, Abbod Maysam F, Shieh Jiann-Shing
Key Laboratory of Fiber Optic Sensing Technology and Information Processing (Wuhan University of Technology), Ministry of Education, Wuhan, China.
School of Information Engineering, Wuhan University of Technology, Wuhan, China.
PeerJ. 2017 Nov 16;5:e4067. doi: 10.7717/peerj.4067. eCollection 2017.
Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying level of consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of consciousness level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.
麻醉深度（DoA）评估在临床手术中至关重要。目前，源自脑电图（EEG）的指标被广泛用于量化麻醉深度。然而，已知在某些情况下这些指标并不准确；因此，经验丰富的麻醉医生依靠监测体温、脉搏率、呼吸率和血压等生命体征来控制手术过程。由于缺乏量化意识水平的理想方法，人们开展了多项研究以开发改进的麻醉深度测量方法。在本研究中，提出了一种称为相似性和分布指数（SDI）的短期指标。SDI是利用时域中的心率变异性（HRV）生成的，并且基于对两个连续32秒HRV数据段之间数据分布差异的观察。SDI结果与意识水平专家评估之间的比较表明，SDI与麻醉深度具有很强的相关性。为了优化效果，构建了人工神经网络（ANN）模型以拟合SDI，并进行了ANN盲交叉验证以克服随机误差和过拟合问题。然后采用了集成ANN，结果发现与常用的脑电双频指数相比，集成ANN能提供良好的麻醉深度评估。本研究证明了这种麻醉深度评估方法的有效性，结果表明，在适当情况下，结合使用其他测量方法，利用SDI测量麻醉深度是可行且有意义的。
