EEG signal features extraction based on fractal dimension

The spread of electroencephalography (EEG) in countless applications has fostered the development of new techniques for extracting synthetic and informative features from EEG signals. However, the definition of an effective feature set depends on the specific problem to be addressed and is currently an active field of research. In this work, we investigated the application of features based on fractal dimension to a problem of sleep identification from EEG data. We demonstrated that features based on fractal dimension, including two novel indices defined in this work, add valuable information to standard EEG features and significantly improve sleep identification performance.

[1]  G. Borghini,et al.  Neuroscience and Biobehavioral Reviews , 2022 .

[2]  Mattia Zanon,et al.  Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach , 2014 .

[3]  S. L. Shishkin,et al.  Application of the change-point analysis to the investigation of the brain’s electrical activity , 2000 .

[4]  Alfredo Ruggeri,et al.  Automatic evaluation of corneal nerve tortuosity in images from in vivo confocal microscopy. , 2011, Investigative ophthalmology & visual science.

[5]  D. L. Schomer,et al.  Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields , 2012 .

[6]  T. Higuchi.  Approach to an irregular time series on the basis of the fractal theory , 1988 .

[7]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[8]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[9]  Brian Klinkenberg,et al.  A review of methods used to determine the fractal dimension of linear features , 1994 .

[10]  Marina Ronzhina,et al.  Sleep scoring using artificial neural networks. , 2012, Sleep medicine reviews.

[11]  Amjed S. Al-Fahoum,et al.  Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains , 2014, ISRN neuroscience.

[12]  Konstantina S. Nikita,et al.  Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection , 2008, BIBE.

[13]  Hiie Hinrikus,et al.  Spectral Asymmetry and Higuchi's Fractal Dimension Measures of Depression Electroencephalogram , 2013, Comput. Math. Methods Medicine.

[14]  W. Pritchard,et al.  Measuring Chaos in the Brain - A Tutorial Review of EEG Dimension Estimation , 1995, Brain and Cognition.

[15]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[16]  P. Agostino Accardo,et al.  Use of the fractal dimension for the analysis of electroencephalographic time series , 1997, Biological Cybernetics.