Article
Mathematics, Interdisciplinary Applications
Guancen Lin, Aijing Lin
Summary: This paper proposes an innovative multiscale sample entropy based on the horizontal visibility graph to measure the complexity of time series, as well as an improved multiscale cross-sample entropy method to measure the synchronization between two time series. By applying these methods to feature extraction, classification, and sleep stage division of EEG signals, it is possible to effectively monitor human health and assess physical status.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Engineering, Mechanical
Yue Wu, Pengjian Shang, Jianan Xia
Summary: The paper introduces a modified sample entropy (SE) method called inverse sample entropy (ISE) for investigating the complexity of financial time series, which considers far neighbors of templates and provides more comprehensive information. Experiments show that ISE is more flexible in threshold selection than SE and more robust to high dimension analysis. This allows ISE to be applied to a wider range of data and extended to high dimension analysis applications.
NONLINEAR DYNAMICS
(2021)
Article
Psychiatry
Baihan Wang, Haritz Irizar, Johan H. Thygesen, Eirini Zartaloudi, Isabelle Austin-Zimmerman, Anjali Bhat, Jasmine Harju-Seppaenen, Oliver Pain, Nick Bass, Vasiliki Gkofa, Behrooz Z. Alizadeh, Therese van Amelsvoort, Maria J. Arranz, Stephan Bender, Wiepke Cahn, Maria Stella Calafato, Benedicto Crespo-Facorro, Marta Di Forti, Ina Giegling, Lieuwe de Haan, Jeremy Hall, Mei-Hua Hall, Neeltje van Haren, Conrad Iyegbe, Rene S. Kahn, Eugenia Kravariti, Stephen M. Lawrie, Kuang Lin, Jurjen J. Luykx, Ignacio Mata, Colm McDonald, Andrew M. McIntosh, Robin M. Murray, Marco Picchioni, John Powell, Diana P. Prata, Dan Rujescu, Bart P. F. Rutten, Madiha Shaikh, Claudia J. P. Simons, Timothea Toulopoulou, Matthias Weisbrod, Ruud van Winkel, Karoline Kuchenbaecker, Andrew McQuillin, Elvira Bramon
Summary: This study aims to identify biological mechanisms relevant to endophenotypes by partitioning polygenic risk scores into specific gene sets. The results show that a reduced P300 amplitude is associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set. This finding suggests that certain genetic variants influence early brain development and increase the risk of schizophrenia.
SCHIZOPHRENIA BULLETIN
(2023)
Article
Computer Science, Interdisciplinary Applications
Daniela M. Zolezzi, Luz Maria Alonso-Valerdi, David I. Ibarra-Zarate
Summary: This study aimed to determine whether nonlinear EEG features such as approximate entropy (ApEn) would better differentiate pain severity than absolute band power. The results showed that ApEn effectively characterizes the different severities of chronic neuropathic pain (NP) rather than the commonly used linear features. ApEn and other nonlinear techniques might be a more suitable methodology to monitor chronic NP experience.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Mathematics, Applied
Arthur Matsuo Yamashita Rios de Sousa, Jaroslav Hlinka
Summary: We extended Elsinger's work on chi-squared tests for independence using ordinal patterns and investigated a general class of m-dependent ordinal patterns processes. We proposed a test method to quantify the range of serial dependence in a process, and applied it to epilepsy electroencephalography time series data.
Article
Physics, Multidisciplinary
Yu Chen, Guang Ling, Xiangxiang Song, Wenhui Tu
Summary: Complex network approaches in nonlinear time series analysis reveal aspects that classic analytic methods fail to reveal. This paper focuses on ordinal pattern transition networks (OPTN) to characterize the statistical complexity of time series. It introduces a new technique, permutation weighted statistical transition entropy (PWSTE), to address the limitations of existing complexity measures. The proposed algorithm is shown to be more sensitive and effective in identifying dynamic changes in simulated and medical data.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Yung-Hung Wang, I-Yu Chen, Herming Chiueh, Sheng-Fu Liang
Summary: Sample entropy (SpEn) is a measure of system complexity achieved by assessing the entropy of a time series, widely used in biomedical measurements and other applications. The assisted sliding box (SBOX) algorithm proposed in this study accelerates the computation of SpEn, reducing power consumption and computation time, making it suitable for embedded systems.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Mechanical
Yuxing Li, Bo Geng, Bingzhao Tang
Summary: Recently, a new method called coded dispersion entropy (CDE) is proposed to improve the noise immunity by coding and partitioning the dispersion patterns in dispersion entropy (DE). The simplified CDE (SCDE) is also proposed to reduce computational consumption while maintaining effectiveness. Simulation experiments demonstrate the advantages of SCDE and CDE in detecting nonlinear dynamic changes, and real-world experiments show that SCDE has better performance in medical diagnosis, fault diagnosis, and signal classification.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mai Jianbiao, Wang Xinzui, Li Zhaobo, Liu Juan, Zhang Zhongwei, Fu Hui
Summary: This study investigates the differences in the specificity of EEG signals between tinnitus patients and healthy subjects by combining time-frequency domain and non-linear power analysis. The results show that tinnitus patients have higher entropy values in certain frequency bands, mainly concentrated in specific brain regions. Finally, the use of support vector machines and optimal feature combinations achieves objective recognition of tinnitus disorders with increased accuracy.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
(2023)
Article
Neurosciences
Laura Katus, Anna Blasi, Sam McCann, Luke Mason, Ebrima Mbye, Ebou Touray, Muhammed Ceesay, Michelle de Haan, Sophie E. Moore, Clare E. Elwell, Sarah Lloyd-Fox
Summary: This study examined habituation and novelty detection in infants using EEG and fNIRS measurements. The results showed weak to medium positive correlations between the two modalities for indices of habituation and novelty detection at different age points. The study suggests that common neural metrics can be extracted across a wide age range in infants, despite the use of different testing modalities and stimuli.
Article
Physiology
Alessia Cacciotti, Chiara Pappalettera, Francesca Miraglia, Lavinia Valeriani, Elda Judica, Paolo Maria Rossini, Fabrizio Vecchio
Summary: The aim of this study was to analyze the effects of CHF on brain activity through EEG complexity measures. The results showed significant differences between CHF patients and healthy elderly individuals in the total spectrum and theta frequency band of the EEG. Within the CHF group, correlations were found between EEG parameters and clinical data such as BNP and NYHA scores. These findings suggest similarities between EEG abnormalities in CHF and cognitive impairment.
Article
Computer Science, Artificial Intelligence
Mostafa Rostaghi, Mohammad Mahdi Khatibi, Mohammad Reza Ashory, Hamed Azami
Summary: Entropy is a powerful tool for nonlinear analysis of time series, but it is sensitive to parameters and can be influenced by noise. To address these issues, we developed fuzzy dispersion entropy, which combines fuzzy membership functions and Shannon entropy. We demonstrated the advantages of fuzzy dispersion entropy over traditional dispersion entropy in detecting the dynamical variability of signals. The results showed that fuzzy dispersion entropy has lower sensitivity to noise and signal length. It was also successfully applied to various real-world applications.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Si Thu Aung, Yodchanan Wongsawat
Summary: This paper introduces a new method called M-mDistEn for detecting motion artifacts in EEG signals, demonstrating high accuracy and statistical significance in differentiating noisy and clean signal portions.
Article
Engineering, Electrical & Electronic
Khandaker Noman, Yongbo Li, Shun Wang
Summary: Sample entropy (SE) is a nonlinear measure used to characterize the health status of rolling element bearings by measuring the complexity of vibration signals. However, in continuous monitoring scenarios under noisy conditions, using SE directly can lead to inefficient early fault warning and an inability to differentiate between different fault types. To address this issue, a new measure called oscillatory sample entropy (OSE) is proposed, which separates the principal component of the vibration signal that is sensitive to SE calculation using the tunable Q factor wavelet transform (TQWT). Experimental case studies have shown that OSE not only overcomes the limitations of SE but also outperforms approximate entropy (AE) and fuzzy entropy (FE) for continuous monitoring of bearing health.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Statistics & Probability
Florian Gunsilius, Susanne Schennach
Summary: This article introduces a generalization of PCA to nonlinear settings, providing a new tool for dimension reduction and exploratory data analysis. The method has unique features and can be effectively applied in fields such as financial prediction.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
