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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Vydané v:IEEE transactions on biomedical circuits and systems Ročník 13; číslo 4; s. 658 - 669
Hlavní autori: Saadeh, Wala, Khan, Fatima Hameed, Altaf, Muhammad Awais Bin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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 machine learning classification processor for accurate DoA estimation irrespective of the patient's age and anesthetic drug. The classification is solely based on six features extracted from EEG signal, i.e., spectral edge frequency (SEF), beta ratio, and four bands of spectral energy (FBSE). A machine learning fine decision tree classifier is adopted to achieve a four-class DoA classification (deep, moderate, and light DoA versus awake state). The feature selection and the classification processor are optimized to achieve the highest classification accuracy for the state of moderate anesthesia required for the surgical operations. The proposed 256-point fast Fourier transform accelerator is implemented to realize SEF, beta ratio, and FBSE that enables minimal latency and high accuracy feature extraction. The proposed DoA processor is implemented using a 65 nm CMOS technology and experimentally verified using field programming gate array (FPGA) based on the EEG recordings of 75 patients undergoing elective surgery with different types of anesthetic agents. The processor achieves an average accuracy of 92.2% for all DoA states, with a latency of 1s The 0.09 mm 2 DoA processor consumes 140nJ/classification.
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ISSN:1932-4545
1940-9990
1940-9990
DOI:10.1109/TBCAS.2019.2921875