Phase space electroencephalography (EEG)

A new mode of intraoperative EEG analysis

R. C. Watt, Stuart R Hameroff

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

Intraoperative monitoring of electroencephalography (EEG) data can help assess brain integrity and/or depth of anesthesia. We demonstrate a computer generated technique which provides a visually robust display of EEG data plotted as 'phase space trajectories' and a mathematically derived parameter ('dimensionality') which may correlate with depth of anesthesia. Application of nonlinear mathematical analysis, used to describe complex dynamical systems, can characterize 'phase space' EEG patterns by identifying attractors (geometrical patterns in phase space corresponding to specific ordered EEG data subjects) and by quantifying the degree of order and chaos (calculation of dimensionality). Dimensionality calculations describe the degree of complexity in a signal and may generate a clinically useful univariate EEG descriptor of anesthetic depth. In this paper we describe and demonstrate phase space trajectories generated for sine waves, mixtures of sine waves, and white noise (random chaotic events). We also present EEG phase space trajectories and dimensionality calculations from a patient undergoing surgery and general anesthesia in 3 recognizable states: awake, anesthetized, and burst suppression. Phase space trajectories of the three states are visually distinguishable, and dimensionality calculations indicate that EEG progresses from 'chaos' (awake) to progressively more 'ordered' attractors (anesthetized and burst suppression).

Original languageEnglish (US)
Pages (from-to)3-13
Number of pages11
JournalInternational Journal of Clinical Monitoring and Computing
Volume5
Issue number1
DOIs
StatePublished - Mar 1988

Fingerprint

Electroencephalography
Anesthesia
Data Display
Intraoperative Monitoring
General Anesthesia
Anesthetics
Brain

Keywords

  • anesthetic depth monitoring
  • dimensionality
  • electroencephalography (EEG)-intraoperative
  • nonlinear dynamics
  • phase space

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine
  • Medicine (miscellaneous)

Cite this

@article{7d97660df720474cbf5af3d79d652478,
title = "Phase space electroencephalography (EEG): A new mode of intraoperative EEG analysis",
abstract = "Intraoperative monitoring of electroencephalography (EEG) data can help assess brain integrity and/or depth of anesthesia. We demonstrate a computer generated technique which provides a visually robust display of EEG data plotted as 'phase space trajectories' and a mathematically derived parameter ('dimensionality') which may correlate with depth of anesthesia. Application of nonlinear mathematical analysis, used to describe complex dynamical systems, can characterize 'phase space' EEG patterns by identifying attractors (geometrical patterns in phase space corresponding to specific ordered EEG data subjects) and by quantifying the degree of order and chaos (calculation of dimensionality). Dimensionality calculations describe the degree of complexity in a signal and may generate a clinically useful univariate EEG descriptor of anesthetic depth. In this paper we describe and demonstrate phase space trajectories generated for sine waves, mixtures of sine waves, and white noise (random chaotic events). We also present EEG phase space trajectories and dimensionality calculations from a patient undergoing surgery and general anesthesia in 3 recognizable states: awake, anesthetized, and burst suppression. Phase space trajectories of the three states are visually distinguishable, and dimensionality calculations indicate that EEG progresses from 'chaos' (awake) to progressively more 'ordered' attractors (anesthetized and burst suppression).",
keywords = "anesthetic depth monitoring, dimensionality, electroencephalography (EEG)-intraoperative, nonlinear dynamics, phase space",
author = "Watt, {R. C.} and Hameroff, {Stuart R}",
year = "1988",
month = "3",
doi = "10.1007/BF01739226",
language = "English (US)",
volume = "5",
pages = "3--13",
journal = "Journal of Clinical Monitoring and Computing",
issn = "1387-1307",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Phase space electroencephalography (EEG)

T2 - A new mode of intraoperative EEG analysis

AU - Watt, R. C.

AU - Hameroff, Stuart R

PY - 1988/3

Y1 - 1988/3

N2 - Intraoperative monitoring of electroencephalography (EEG) data can help assess brain integrity and/or depth of anesthesia. We demonstrate a computer generated technique which provides a visually robust display of EEG data plotted as 'phase space trajectories' and a mathematically derived parameter ('dimensionality') which may correlate with depth of anesthesia. Application of nonlinear mathematical analysis, used to describe complex dynamical systems, can characterize 'phase space' EEG patterns by identifying attractors (geometrical patterns in phase space corresponding to specific ordered EEG data subjects) and by quantifying the degree of order and chaos (calculation of dimensionality). Dimensionality calculations describe the degree of complexity in a signal and may generate a clinically useful univariate EEG descriptor of anesthetic depth. In this paper we describe and demonstrate phase space trajectories generated for sine waves, mixtures of sine waves, and white noise (random chaotic events). We also present EEG phase space trajectories and dimensionality calculations from a patient undergoing surgery and general anesthesia in 3 recognizable states: awake, anesthetized, and burst suppression. Phase space trajectories of the three states are visually distinguishable, and dimensionality calculations indicate that EEG progresses from 'chaos' (awake) to progressively more 'ordered' attractors (anesthetized and burst suppression).

AB - Intraoperative monitoring of electroencephalography (EEG) data can help assess brain integrity and/or depth of anesthesia. We demonstrate a computer generated technique which provides a visually robust display of EEG data plotted as 'phase space trajectories' and a mathematically derived parameter ('dimensionality') which may correlate with depth of anesthesia. Application of nonlinear mathematical analysis, used to describe complex dynamical systems, can characterize 'phase space' EEG patterns by identifying attractors (geometrical patterns in phase space corresponding to specific ordered EEG data subjects) and by quantifying the degree of order and chaos (calculation of dimensionality). Dimensionality calculations describe the degree of complexity in a signal and may generate a clinically useful univariate EEG descriptor of anesthetic depth. In this paper we describe and demonstrate phase space trajectories generated for sine waves, mixtures of sine waves, and white noise (random chaotic events). We also present EEG phase space trajectories and dimensionality calculations from a patient undergoing surgery and general anesthesia in 3 recognizable states: awake, anesthetized, and burst suppression. Phase space trajectories of the three states are visually distinguishable, and dimensionality calculations indicate that EEG progresses from 'chaos' (awake) to progressively more 'ordered' attractors (anesthetized and burst suppression).

KW - anesthetic depth monitoring

KW - dimensionality

KW - electroencephalography (EEG)-intraoperative

KW - nonlinear dynamics

KW - phase space

UR - http://www.scopus.com/inward/record.url?scp=0023901756&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0023901756&partnerID=8YFLogxK

U2 - 10.1007/BF01739226

DO - 10.1007/BF01739226

M3 - Article

VL - 5

SP - 3

EP - 13

JO - Journal of Clinical Monitoring and Computing

JF - Journal of Clinical Monitoring and Computing

SN - 1387-1307

IS - 1

ER -