prikaz prve stranice dokumenta Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis
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Scientific paper - Original scientific paper
Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis
Computers in biology and medicine, 80 (2017); 1-13. https://doi.org/10.1016/j.compbiomed.2016.11.002
Lerga, Jonatan; Saulig, Nicoletta; Mozetič, Vladimir

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Lerga, J., Saulig, N. & Mozetič, V. (2017). Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis. Computers in biology and medicine, 80., 1-13. doi: 10.1016/j.compbiomed.2016.11.002

Lerga, Jonatan, et al. "Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis." Computers in biology and medicine, vol. 80, 2017, pp. 1-13. https://doi.org/10.1016/j.compbiomed.2016.11.002

Lerga, Jonatan, Nicoletta Saulig and Vladimir Mozetič. "Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis." Computers in biology and medicine 80 (2017): 1-13. https://doi.org/10.1016/j.compbiomed.2016.11.002

Lerga, J., Saulig, N. and Mozetič, V. (2017) 'Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis', Computers in biology and medicine, 80, pp. 1-13. doi: 10.1016/j.compbiomed.2016.11.002

Lerga J, Saulig N, Mozetič V. Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis. Computers in biology and medicine [Internet]. 2017 [cited 2024 December 12];80:1-13. doi: 10.1016/j.compbiomed.2016.11.002

J. Lerga, N. Saulig and V. Mozetič, "Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis", Computers in biology and medicine, vol. 80, pp. 1-13, 2017. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:184:782424. [Accessed: 12 December 2024]

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Metadata
Title (english)Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis
AuthorJonatan Lerga
MBZ: 298775
AuthorNicoletta Saulig
MBZ: 314984
AuthorVladimir Mozetič
Author's institutionUniversity of Rijeka
Faculty of Medicine
(Department of Social Medicine and Epidemiology)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract (english)
Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionAccepted version
Journal titleComputers in biology and medicine
Numberingvol. 80, pp. 1-13
p-ISSN0010-4825
DOIhttps://doi.org/10.1016/j.compbiomed.2016.11.002
URN:NBNurn:nbn:hr:184:782424
Publication2017
Document URLhttp://bib.irb.hr/849306
Type of resourceText
Access conditionsInstitutional access
Terms of useLicence In copyright icon
Created on2022-02-04 10:52:41