In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 μW per channel and has the input-referred noise of 0.29 μVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.

中文翻译：

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基于具有大EDO耐受性EEG模拟前端的深层神经网络的麻醉深度实时监控系统。

在本文中，我们将结合深度学习框架AnesNET提出基于实时脑电图（EEG）的麻醉深度（DoA）监控系统。在提出的系统中实现了一种EEG模拟前端（AFE），该前端可以使用粗略的数字DC伺服环路来补偿±380 mV的电极DC偏移。引入基于EEG的MAC EEGMAC作为一种准确预测DoA的新指标，该指标旨在应用于由挥发性和静脉内药物麻醉的患者。拟议的深度学习协议包括四层卷积神经网络和两层密集层。此外，我们优化了深度神经网络（DNN）的复杂性，使其能够在Raspberry Pi 3等微型计算机上运行，​​从而实现了具有成本效益的小型DoA监控系统。采用110纳米CMOS制造，原型AFE的每通道功耗为4.33μW，在0.5至100 Hz范围内具有0.29μVrms的输入参考噪声，噪声效率系数为2.2。拟议的DNN用预先记录的374例接受手术麻醉的吸入麻醉剂治疗的EEG数据进行了评估，分别得出均方根误差和绝对误差分别为0.048和0.05。用静脉内麻醉剂麻醉的受试者的EEGMAC也显示出与双光谱指数值的良好一致性，证实了拟议的DoA指数适用于两种麻醉药。使用Raspberry Pi 3实施的监视系统可在20 ms内估算EEGMAC，这比文献中的BIS估算快约1000倍。5至100 Hz，噪声效率系数为2.2。拟议的DNN用预先记录的374例接受手术麻醉的吸入麻醉剂治疗的EEG数据进行了评估，分别得出均方根误差和绝对误差分别为0.048和0.05。用静脉内麻醉剂麻醉的受试者的EEGMAC也显示出与双光谱指数值的良好一致性，证实了拟议的DoA指数适用于两种麻醉药。使用Raspberry Pi 3实施的监视系统可在20 ms内估算EEGMAC，这比文献中的BIS估算快约1000倍。5至100 Hz，噪声效率系数为2.2。拟议的DNN用预先记录的374例接受手术麻醉的吸入麻醉剂治疗的EEG数据进行了评估，分别得出均方根误差和绝对误差分别为0.048和0.05。用静脉内麻醉剂麻醉的受试者的EEGMAC也显示出与双光谱指数值的良好一致性，证实了拟议的DoA指数适用于两种麻醉药。使用Raspberry Pi 3实施的监视系统可在20 ms内估算EEGMAC，这比文献中的BIS估算快约1000倍。用静脉内麻醉剂麻醉的受试者的EEGMAC也显示出与双光谱指数值的良好一致性，证实了拟议的DoA指数适用于两种麻醉药。使用Raspberry Pi 3实施的监视系统可在20 ms内估算EEGMAC，这比文献中的BIS估算快约1000倍。用静脉内麻醉剂麻醉的受试者的EEGMAC也显示出与双光谱指数值的良好一致性，证实了拟议的DoA指数适用于两种麻醉剂。使用Raspberry Pi 3实施的监视系统可在20 ms内估算EEGMAC，这比文献中的BIS估算快约1000倍。