Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia

Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electroencephalograph (EEG)-based DoA monitors are recommended only for certain anesthetic drugs and specific age-group patients. This paper presents a...

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Bibliographic Details
Published in:	IEEE transactions on biomedical circuits and systems Vol. 13; no. 4; pp. 658 - 669
Main Authors:	Saadeh, Wala, Khan, Fatima Hameed, Altaf, Muhammad Awais Bin
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Adult Age Aged Algorithms Anesthesia Artificial intelligence Classification CMOS Decision trees depth of anesthesia Direction-of-arrival estimation EEG electroencephalogram (EEG) Electroencephalography Equipment Design Estimation Fast Fourier transformations Feature extraction Field programmable gate arrays fine decision tree (FDT) Fourier Analysis Fourier transforms Humans Latency Learning algorithms Machine Learning machine learning classifier Microprocessors Middle Aged Monitoring ROC Curve Surgery
ISSN:	1932-4545, 1940-9990, 1940-9990
Online Access:	Get full text
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