A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End
- 주제(키워드) Electroencephalography , Monitoring , Anesthesia , DSL , Indexes , Direction-of-arrival estimation , Electrodes , Bispectral index , convolutional neural network , depth of anesthesia monitoring , electrode DC offset , electroencephalogram , latency , minimum alveolar concentration , Raspberry Pi 3
- 주제(기타) Engineering, Biomedical
- 주제(기타) Engineering, Electrical & Electronic
- 설명문(일반) [Park, Yongjae; Han, Su-Hyun; Kim, Seong-Jin] Ulsan Natl Inst Sci & Technol, Sch Elect & Comp Engn, Ulsan 44919, South Korea; [Byun, Wooseok] Chungnam Natl Univ, Dept Elect Engn, Deajeon 34134, South Korea; [Kim, Ji-Hoon] Ewha Woman Univ, Dept Elect & Elect Engn, Seoul 03760, South Korea; [Lee, Hyung-Chul] Seoul Natl Univ, Seoul Natl Univ Hosp, Coll Med, Dept Anesthesiol & Pain Med, Seoul 03080, South Korea
- 후원정보 IEEE, IEEE Circuits & Syst Soc, IEEE Engn Med & Biol Soc, IEEE Solid State Circuits Soc, Tateisi Sci & Technol Fdn, Nanolux, Nidek Co Ltd, Hisol, Keysight Technologies, Horiba Adv Techno, Maxwell Biosystems, Sysmex, Kyocera, Shimadzu, Santen, Omron, ThorLabs, Hamamatsu, Mdpi, Micromachines
- 등재 SCIE, SCOPUS
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2020
- 총서유형 Journal
- 회의명 IEEE Biomedical Circuits and Systems Conference (BioCAS)
- 개최지 Nara, JAPAN
- 일자 OCT 17-19, 2019
- URI http://www.dcollection.net/handler/ewha/000000174580
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/TBCAS.2020.2998172
- PubMed https://pubmed.ncbi.nlm.nih.gov/32746339
초록/요약
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 mu W per channel and has the input-referred noise of 0.29 mu Vrms 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.
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