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项目作者： otoolej

项目描述 ：
NEURAL: a neonatal EEG feature set in Matlab
高级语言： MATLAB
项目地址: git://github.com/otoolej/qEEG_feature_set.git
创建时间： 2016-05-06T17:32:56Z
项目社区：https://github.com/otoolej/qEEG_feature_set
开源协议：BSD 3-Clause "New" or "Revised" License
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arXiv

NEURAL: A Neonatal EEG Feature Set in Matlab

Matlab code to generate a set of quantitative features from multichannel EEG
recordings. Features include amplitude measures, spectral measures, and basic connectivity
measures (across hemisphere’s only). Also, for preterm EEG (assuming gestational age < 32
weeks), will generate features from bursts annotations (e.g. maximum inter-burst
interval). Burst annotations require a separate package, also available on
github.

Releases archived at Zenodo:
DOI

Full details of the methods are in:

JM O’Toole and GB Boylan (2017). NEURAL: quantitative features for newborn EEG using Matlab. ArXiv e-prints, arXiv:1704.05694

which is available at https://arxiv.org/abs/1704.05694.

Requirements | Use | Quantitative
features | Files | Test computer
setup | Licence | References |
Contact

Requirements

Matlab (R2013 or newer,
Mathworks) with the signal
processing toolbox and statistics toolbox. Not tested with Octave but should work with minor
tweaking.

Use

Set paths in Matlab, or do so using the load_curdir function:

  1.   >> load_curdir;

As an example, generate simulated EEG and calculate relative spectral power, standard
deviation of range-EEG, and brain symmetry index:

  1.     % generate EEG-like data (coloured Gaussian noise)
  2.     data_st=gen_test_EEGdata(5*60,64,1);
  4.     % define feature set (or can define in neural_parameters.m):
  5.     feature_set={'spectral_relative_power','rEEG_SD', 'connectivity_BSI'};
  7.     % estimate features:
  8.     feat_st=generate_all_features(data_st,[],feature_set);

See the demos/ directory for further examples. All parameters are set the file
neural_parameters.m.

Quantitative features

The feature set contains amplitude, spectral, connectivity, and burst annotation features.
Amplitude features include range-EEG (D. O’ Reilly et al., 2012;
see references), a clearly-defined alternative to amplitude-integrated EEG
(aEEG). All features are generated for four different frequency bands (typically 0.5–4,
4–7, 7–13, and 13–30 Hz), with some exceptions. The following table describes the features
in more detail:

feature name description FB
spectral_power spectral power: absolute yes
spectral_relative_power spectral power: relative (normalised to total spectral power) yes
spectral_flatness spectral entropy: Wiener (measure of spectral flatness) yes
spectral_entropy spectral entropy: Shannon yes
spectral_diff difference between consecutive short-time spectral estimates yes
spectral_edge_frequency cut-off frequency (fc): 95% of spectral power contained between 0.5 and fc Hz no
FD fractal dimension yes
amplitude_total_power time-domain signal: total power yes
amplitude_SD time-domain signal: standard deviation yes
amplitude_skew time-domain signal: skewness (absolute value) yes
amplitude_kurtosis time-domain signal: kurtosis yes
amplitude_env_mean envelope: mean value yes
amplitude_env_SD envelope: standard deviation (SD) yes
connectivity_BSI brain symmetry index (see Van Putten 2007) yes
connectivity_corr correlation (Spearman) between envelopes of hemisphere-paired channels yes
connectivity_coh_mean coherence: mean value yes
connectivity_coh_max coherence: maximum value yes
connectivity_coh_freqmax coherence: frequency of maximum value yes
rEEG_mean range EEG: mean yes
rEEG_median range EEG: median yes
rEEG_lower_margin range EEG: lower margin (5th percentile) yes
rEEG_upper_margin range EEG: upper margin (95th percentile) yes
rEEG_width range EEG: upper margin - lower margin yes
rEEG_SD range EEG: standard deviation yes
rEEG_CV range EEG: coefficient of variation yes
rEEG_asymmetry range EEG: measure of skew about median yes
IBI_length_max burst annotation: maximum (95th percentile) inter-burst interval no
IBI_length_median burst annotation: median inter-burst interval no
IBI_burst_prc burst annotation: burst percentage no
IBI_burst_number burst annotation: number of bursts no

FB: features generated for each frequency band (FB)

Files

Some Matlab files (.m files) have a description and an example in the header. To read this
header, type help <filename.m> in Matlab. Directory structure is as follows: 

  1. ├── amplitude_features/                  # amplitude features
  2. ├── spectral_features/                   # spectral features
  3. ├── connectivity_features/               # hemisphere connectivity features
  4. ├── range_EEG/                           # range-EEG (similar to aEEG)
  5. ├── IBI_features/                        # features from the burst annotations 
  6. ├── preprocessing/                       # loads EEG from EDF files (including artefact removal)
  7. └── utils/                               # misc. functions

with some files of interest:

  1. ├── neural_parameters.m                  # all parameters defined here
  2. ├── all_features_list.m                  # complete list of functions (do not edit)
  3. └── generate_all_features.m              # main function: generates feature set on EEG

Test computer setup

  • hardware: Intel Core i7-8700K @ 4.5GHz; 32GB memory.
  • operating system: Ubuntu GNU/Linux x86_64 distribution (19.10) with Linux kernel
    5.3.0-24-generic
  • software: Matlab (R2019a)

Licence

  1. Copyright (c) 2016, John M. O' Toole, University College Cork
  2. All rights reserved.
  4. Redistribution and use in source and binary forms, with or without modification,
  5. are permitted provided that the following conditions are met:
  7.   Redistributions of source code must retain the above copyright notice, this
  8.   list of conditions and the following disclaimer.
  10.   Redistributions in binary form must reproduce the above copyright notice, this
  11.   list of conditions and the following disclaimer in the documentation and/or
  12.   other materials provided with the distribution.
  14.   Neither the name of the University College Cork nor the names of its
  15.   contributors may be used to endorse or promote products derived from
  16.   this software without specific prior written permission.
  18. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
  19. ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  20. WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  21. DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
  22. ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
  23. (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
  24. LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
  25. ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  26. (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  27. SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

References

  1. JM O’Toole and GB Boylan (2017). NEURAL: quantitative features for newborn EEG using
    Matlab. ArXiv e-prints, arXiv:1704.05694.

  2. D O’Reilly, MA Navakatikyan, M Filip, D Greene, & LJ Van Marter (2012). Peak-to-peak
    amplitude in neonatal brain monitoring of premature infants. Clinical Neurophysiology,
    123(11):2139–2153.

  3. MJAM van Putten (2007). The revised brain symmetry index. Clinical Neurophysiology,
    118(11):2362–2367.

  4. T Higuchi (1988). Approach to an irregular time series on the basis of the fractal theory,
    Physica D: Nonlinear Phenomena, 31:277–283.

  5. MJ Katz (1988). Fractals and the analysis of waveforms. Computers in Biology and
    Medicine, 18(3):145–156.

  6. AV Oppenheim, RW Schafer. Discrete-Time Signal Processing. Prentice-Hall, Englewood
    Cliffs, NJ 07458, 1999.

  7. JM O’ Toole, GB Boylan, S Vanhatalo, NJ Stevenson (2016). Estimating functional brain
    maturity in very and extremely preterm neonates using automated analysis of the
    electroencephalogram. Clinical Neurophysiology, 127(8):2910–2918

  8. JM O’ Toole, GB Boylan, RO Lloyd, RM Goulding, S Vanhatalo, NJ Stevenson
    (2017). Detecting Bursts in the EEG of Very and Extremely Premature Infants using a
    Multi-Feature Approach. Medical Engineering and
    Physics, vol. 45, pp. 42-50, 2017.
    DOI:10.1016/j.medengphy.2017.04.003

  9. JM O’Toole and GB Geraldine (2019). Quantitative Preterm EEG Analysis: The Need for
    Caution in Using Modern Data Science Techniques. Frontiers in Pediatrics 7, 174
    DOI:10.3389/fped.2019.00174

Contact

John M. O’Toole

Neonatal Brain Research Group,
INFANT Research Centre,
Department of Paediatrics and Child Health,
Room 2.19 UCC Academic Paediatric Unit, Cork University Hospital,
University College Cork,
Ireland

  • email: jotoole AT ucc _dot ie