publication . Preprint . Article . Other literature type . 2018

Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity: Toward Its Use as a Signal Feature for Machine Learning in Clinical Applications.

Louis Lemieux; Matthew Walker; Yujiang Wang; Lucas Gabriel Souza França;
Open Access English
  • Published: 10 Dec 2018
  • Country: United Kingdom
Abstract
Comment: Final version published at Frontiers in Physiology. https://doi.org/10.3389/fphys.2018.01767
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Quantitative Biology - Neurons and Cognition, Condensed Matter - Statistical Mechanics, Computer Science - Information Theory, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Nonlinear Sciences - Chaotic Dynamics, EEG, fractal, multifractal, epilepsy, variance, characteristic time, Physiology, QP1-981, Methods, signal features, machine learning, EEG, fractal, multifractal, epilepsy, variance, characteristic time, signal features, machine learning
111 references, page 1 of 8

Acharya, R., Faust, O., Kannathal, N., Chua, T., and Laxminarayan, S. (2005). Nonlinear analysis of EEG signals at various sleep stages. Comput. Methods Prog. Biomed. 80, 37-45. doi: 10.1016/j.cmpb.2005.06.011

Argoul, F., Arnéodo, A., Grasseau, G., Gagne, Y., Hopfinger, E. J., and Frisch, U. (1989). Wavelet analysis of turbulence reveals the multifractal nature of the Richardson cascade. Nature 338, 51-53. doi: 10.1038/33 8051a0 [OpenAIRE]

Bak, P., and Paczuski, M. (1995). Complexity, contingency, and criticality. Proc. Natl. Acad. Sci. U.S.A. 92, 6689-6896. doi: 10.1073/pnas.92.15.6689 [OpenAIRE]

Bak, P., Tang, C., and Wiesenfeld, K. (1987). Self-organized criticality: an explanation of the 1/ f noise. Phys. Rev. Lett. 59, 381-384. doi: 10.1103/PhysRevLett.59.381

Baldassano, S. N., Brinkmann, B. H., Ung, H., Blevins, T., Conrad, E. C., Leyde, K., et al. (2017). Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings. Brain 140, 1680-1691. doi: 10.1093/brain/awx098 [OpenAIRE]

Bassett, D. S., Greenfield, D. L., Meyer-Lindenberg, A., Weinberger, D. R., Moore, S. W., and Bullmore, E. T. (2010). Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comput. Biol. 6:e1000748. doi: 10.1371/journal.pcbi.1000748

Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., and Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl. Acad. Sci. U.S.A. 103, 19518-19523. doi: 10.1073/pnas.0606005103

Beggs, J. M., and Plenz, D. (2003). Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177. doi: 10.1523/JNEUROSCI.23-35-11167.2003 [OpenAIRE]

Beggs, J. M., and Plenz, D. (2004). Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J. Neurosci. 24, 5216-5229. doi: 10.1523/JNEUROSCI.0540-04.2004 [OpenAIRE]

Bengtsson, H. (2016). R.matlab: Read and Write Mat Files and Call Matlab From Within R. R package version 3.6.1.

Bianco, S., Ignaccolo, M., Rider, M. S., Ross, M. J., Winsor, P., and Grigolini, P. (2007). Brain, music, and non-Poisson renewal processes. Phys. Rev. E 75:061911. doi: 10.1103/PhysRevE.75.061911

Biswas, A. P. H., and Si, C. (2012). “Application of multifractal and joint multifractal analysis in examining soil spatial variation: a review,” in Fractal Analysis and Chaos in Geosciences, ed S.-A. Ouadfeul (InTech), 109-138. doi: 10.5772/51437 [OpenAIRE]

Blum, A. L., and Langley, P. (1997). Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245-271.

Brinkmann, B. H., Bower, M. R., Stengel, K. A., Worrell, G. A., and Stead, M. (2009). Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data. J. Neurosci. Methods 180, 185-192. doi: 10.1016/j.jneumeth.2009.03.022

Brinkmann, B. H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S. C., Chen, M., et al. (2016). Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713-1722. doi: 10.1093/brain/aww045

111 references, page 1 of 8
Abstract
Comment: Final version published at Frontiers in Physiology. https://doi.org/10.3389/fphys.2018.01767
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Quantitative Biology - Neurons and Cognition, Condensed Matter - Statistical Mechanics, Computer Science - Information Theory, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Nonlinear Sciences - Chaotic Dynamics, EEG, fractal, multifractal, epilepsy, variance, characteristic time, Physiology, QP1-981, Methods, signal features, machine learning, EEG, fractal, multifractal, epilepsy, variance, characteristic time, signal features, machine learning
111 references, page 1 of 8

Acharya, R., Faust, O., Kannathal, N., Chua, T., and Laxminarayan, S. (2005). Nonlinear analysis of EEG signals at various sleep stages. Comput. Methods Prog. Biomed. 80, 37-45. doi: 10.1016/j.cmpb.2005.06.011

Argoul, F., Arnéodo, A., Grasseau, G., Gagne, Y., Hopfinger, E. J., and Frisch, U. (1989). Wavelet analysis of turbulence reveals the multifractal nature of the Richardson cascade. Nature 338, 51-53. doi: 10.1038/33 8051a0 [OpenAIRE]

Bak, P., and Paczuski, M. (1995). Complexity, contingency, and criticality. Proc. Natl. Acad. Sci. U.S.A. 92, 6689-6896. doi: 10.1073/pnas.92.15.6689 [OpenAIRE]

Bak, P., Tang, C., and Wiesenfeld, K. (1987). Self-organized criticality: an explanation of the 1/ f noise. Phys. Rev. Lett. 59, 381-384. doi: 10.1103/PhysRevLett.59.381

Baldassano, S. N., Brinkmann, B. H., Ung, H., Blevins, T., Conrad, E. C., Leyde, K., et al. (2017). Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings. Brain 140, 1680-1691. doi: 10.1093/brain/awx098 [OpenAIRE]

Bassett, D. S., Greenfield, D. L., Meyer-Lindenberg, A., Weinberger, D. R., Moore, S. W., and Bullmore, E. T. (2010). Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comput. Biol. 6:e1000748. doi: 10.1371/journal.pcbi.1000748

Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., and Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl. Acad. Sci. U.S.A. 103, 19518-19523. doi: 10.1073/pnas.0606005103

Beggs, J. M., and Plenz, D. (2003). Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177. doi: 10.1523/JNEUROSCI.23-35-11167.2003 [OpenAIRE]

Beggs, J. M., and Plenz, D. (2004). Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J. Neurosci. 24, 5216-5229. doi: 10.1523/JNEUROSCI.0540-04.2004 [OpenAIRE]

Bengtsson, H. (2016). R.matlab: Read and Write Mat Files and Call Matlab From Within R. R package version 3.6.1.

Bianco, S., Ignaccolo, M., Rider, M. S., Ross, M. J., Winsor, P., and Grigolini, P. (2007). Brain, music, and non-Poisson renewal processes. Phys. Rev. E 75:061911. doi: 10.1103/PhysRevE.75.061911

Biswas, A. P. H., and Si, C. (2012). “Application of multifractal and joint multifractal analysis in examining soil spatial variation: a review,” in Fractal Analysis and Chaos in Geosciences, ed S.-A. Ouadfeul (InTech), 109-138. doi: 10.5772/51437 [OpenAIRE]

Blum, A. L., and Langley, P. (1997). Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245-271.

Brinkmann, B. H., Bower, M. R., Stengel, K. A., Worrell, G. A., and Stead, M. (2009). Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data. J. Neurosci. Methods 180, 185-192. doi: 10.1016/j.jneumeth.2009.03.022

Brinkmann, B. H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S. C., Chen, M., et al. (2016). Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713-1722. doi: 10.1093/brain/aww045

111 references, page 1 of 8
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