A 25.2mW EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring

Unsoo Ha; Jaehyuk Lee; Jihee Lee; Kwantae Kim; Minseo Kim; Taehwan Roh; Sang Sik Choi; Hoi Jun Yoo

doi:10.1109/ISSCC.2017.7870455

A 25.2mW EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring

Unsoo Ha, Jaehyuk Lee, Jihee Lee, Kwantae Kim, Minseo Kim, Taehwan Roh, Sang Sik Choi, Hoi Jun Yoo

Department of Anesthesiology and Pain Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

There has been recent research into continuous monitoring of the quantitative anesthesia (ANES) depth level for safe surgery [1]. However, the current ANES depth monitoring approach, bispectral index (BIS) [3], uses only EEG from the frontal lobe, and it shows critical limitations in the monitoring of ANES depth such as signal distortion due to electrocautery, EMG and dried gel, and false response to the special types of anesthetic drugs [3]. Near-infrared spectroscopy (NIRS) is complementary to EEG [2], and can not only compensate for the distorted depth level, but also assess the effects of various anesthetic drugs. In spite of its importance, a unified ANES monitoring system using EEG/NIRS together has not been reported because NIRS signals have widely different dynamic ranges (10pA to 10nA), and also signal level variations from person to person and environment are not manageable without closed-loop control (CLC).

Original language	English
Title of host publication	2017 IEEE International Solid-State Circuits Conference, ISSCC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	450-451
Number of pages	2
Volume	60
ISBN (Electronic)	9781509037575
DOIs	https://doi.org/10.1109/ISSCC.2017.7870455
Publication status	Published - 2017 Mar 2
Event	64th IEEE International Solid-State Circuits Conference, ISSCC 2017 - San Francisco, United States Duration: 2017 Feb 5 → 2017 Feb 9

Other

Other	64th IEEE International Solid-State Circuits Conference, ISSCC 2017
Country	United States
City	San Francisco
Period	17/2/5 → 17/2/9

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

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A 25.2mW EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring. / Ha, Unsoo; Lee, Jaehyuk; Lee, Jihee; Kim, Kwantae; Kim, Minseo; Roh, Taehwan; Choi, Sang Sik; Yoo, Hoi Jun.

2017 IEEE International Solid-State Circuits Conference, ISSCC 2017. Vol. 60 Institute of Electrical and Electronics Engineers Inc., 2017. p. 450-451 7870455.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ha, U, Lee, J, Lee, J, Kim, K, Kim, M, Roh, T, Choi, SS & Yoo, HJ 2017, A 25.2mW EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring. in 2017 IEEE International Solid-State Circuits Conference, ISSCC 2017. vol. 60, 7870455, Institute of Electrical and Electronics Engineers Inc., pp. 450-451, 64th IEEE International Solid-State Circuits Conference, ISSCC 2017, San Francisco, United States, 17/2/5. https://doi.org/10.1109/ISSCC.2017.7870455

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abstract = "There has been recent research into continuous monitoring of the quantitative anesthesia (ANES) depth level for safe surgery [1]. However, the current ANES depth monitoring approach, bispectral index (BIS) [3], uses only EEG from the frontal lobe, and it shows critical limitations in the monitoring of ANES depth such as signal distortion due to electrocautery, EMG and dried gel, and false response to the special types of anesthetic drugs [3]. Near-infrared spectroscopy (NIRS) is complementary to EEG [2], and can not only compensate for the distorted depth level, but also assess the effects of various anesthetic drugs. In spite of its importance, a unified ANES monitoring system using EEG/NIRS together has not been reported because NIRS signals have widely different dynamic ranges (10pA to 10nA), and also signal level variations from person to person and environment are not manageable without closed-loop control (CLC).",

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A 25.2mW EEG-NIRS multimodal SoC for accurate anesthesia depth monitoring

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