Research on Sleep EEG Signals Based on IOTA
Author: 
Jun WangAuthors Info & Claims
Jun WangAuthors Info & ClaimsArticle No.: 3, Pages 1 - 6
Abstract
As a nonlinear analysis method based on permutation, internal composition alignment (IOTA) algorithm can study the coupling between systems by calculating the coupling coefficient between two time series. In this paper, the internal composition alignment (IOTA) algorithm is used to study the sleep EEG signals generated by the human body in different sleep periods. Firstly, the IOTA coefficients between different time series calculated by this method are used as nodes to construct the sleep function networks in different sleep periods, and the statistical characteristics of networks such as node degree and clustering coefficient are selected to compare different sleep networks. The results show that the IOTA coefficient and the node average degree and aggregation coefficient of EEG network in NREM-I period are higher than those in awake period, indicating that the complexity of EEG network in NREM-I period is higher than that in awake period, and that the coupling degree in NREM-I period is also higher than that in awake period. This experiment proves the effectiveness of IOTA algorithm for analyzing sleep function network. This algorithm can be used to study sleep EEG staging. At the same time, it also provides an important reference for the research, clinical diagnosis and treatment of sleep diseases in the future.
References
[1]
Pachori R B, Bajaj V. Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition[J]. Computer methods and programs in biomedicine. 2011, 104(3): 373-381.
[2]
Tang X, Xia L, Liu W, Analysis of Frequency Domain of EEG Signals in Clinical Location of Epileptic Focus[J]. Clinical EEG and Neuroscience. 2013, 44(1): 25-30.
[3]
Abdulla W, Wong L. Neonatal EEG signal characteristics using time frequency analysis[J]. Physica A Statistical Mechanics & Its Applications. 2011, 390(6): 1096-1110. [2]
[4]
Li X, Xu G, Shang X K, Correlation dimension analysis of EEG signals induced by electrical acupuncture[C], 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI), Hangzhou, 2013: 585-590.
[5]
Chouvarda I, Rosso V, Mendez M O, Assessment of the EEG complexity during activations from sleep[J]. Computer Methods and Programs in Biomedicine, 2011, 104(3): e16-e28.
[6]
Hamida T B, Ahmed B. Computer based sleep staging: Challenges for the future[C]// 2013.
[7]
S H, A K, J K, Inner composition alignment for inferring directed networks from short time series. [J]. Physical Review Letters, 2011, COM-107(no.5): 3214-3219.
[8]
Nawrath J, Romano M C, Thiel M, Distinguishing Direct from Indirect Interactions in Oscillatory Networks with Multiple Time Scales[J]. Physical Review Letters, 2010, 104(3): 038701.
[9]
Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
[10]
Xin X, Yun Z, Qianli M. Research on Simulation Design and Analysis of EEG Data Signals in Matlab Filtering [J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2011, 31(6): 37-43
Index Terms
- Research on Sleep EEG Signals Based on IOTA
Recommendations
Computer Based Synchronization Analysis on Sleep EEG in Insomnia
Inter-hemispheric sleep EEG coherence is studied in 10 subjects with psycho physiological insomnia, in 10 with paradoxical insomnia, and in 10 matched controls through different states of the sleep/wakefulness cycle. Inter hemispheric EEG coherence ...
Identification of Deep Sleep and Awake with Computational EEG Measures
The objective of the present work was to examine identification of deep sleep and awake with computational analysis of sleep EEG traces from central brain regions. All-night EEG traces from a total of 56 male subjects, 22 healthy control subjects and 34 ...
Singular Spectrum Analysis of Sleep EEG in Insomnia
In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra ...
Comments
Information & Contributors
Information
Published In
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 December 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSAE 2022
CSAE 2022: The 6th International Conference on Computer Science and Application Engineering
October 21 - 23, 2022
Virtual Event, China
Acceptance Rates
Overall Acceptance Rate 368 of 770 submissions, 48%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 27Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)1
Reflects downloads up to 22 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format