MEG analysis of neural dynamics in attention-deficit/hyperactivity disorder with fuzzy entropy

Monge, Jesús; Gómez, Carlos; Poza, Jesús; Fernández, Alberto; Quintero, Javier; Hornero, Roberto

doi:10.1016/j.medengphy.2015.02.006

Cited by 26 publications

(25 citation statements)

References 39 publications

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“…However, our results are different from some previous studies which reported relatively decreased complexity of brain activity in ADHD subjects 32, 49 . We inferred that there may be three main reasons.…”

Section: Discussioncontrasting

confidence: 99%

Complexity analysis of fNIRS signals in ADHD children during working memory task

Miao

Han

et al. 2017

Sci Rep

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Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. Neuroimaging studies have revealed abnormalities of neural activities in some brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Recently, some investigators have demonstrated that nonlinear complexity analysis of neural activity may provide a new index to indicate ADHD. In the present study, we used the permutation entropy (PE) to measure the complexity of functional near-infrared spectroscopy (fNIRS) signals in children with and without ADHD during a working memory task, it was aimed to investigate the relationship between the PE values and the cortical activations, and the different PE values between the children with and without ADHD. We found that PE values exhibited significantly negative correlation with the cortical activations (r = −0.515, p = 0.003), and the PE values of right dorsolateral prefrontal cortex in ADHD children were significantly larger than those in normal controls (p = 0.027). In addition, the PE values of right dorsolateral prefrontal cortex were positively correlated to the ADHD index (r = 0.448, p = 0.012). These results suggest that complexity analysis of fNIRS signals could be a promising tool in diagnosing children with ADHD.

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Section: Discussioncontrasting

confidence: 99%

Complexity analysis of fNIRS signals in ADHD children during working memory task

Miao

Han

et al. 2017

Sci Rep

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“…Data were then digitally filtered using a 1–65 Hz band-pass filter and a 50 Hz notch filter. To minimize the presence of oculographic, cardiographic, and myographic artifacts, both visual inspection and independent component analysis (ICA) were performed [ 15 ]. Finally, artifact-free epochs of 848 samples (5 s) were selected for further analyses.…”

Section: Methodsmentioning

confidence: 99%

“…Spectral analysis revealed that ADHD patients showed significantly higher theta relative power and lower beta relative power, along with higher theta/alpha and theta/beta ratios [ 14 ]. Likewise, recent studies suggested that nonlinear measures can complement spectral analysis in order to understand brain dynamics in ADHD [ 15 ]. For instance, MEG data were analyzed by using Lempel-Ziv complexity [ 16 ] and fuzzy entropy [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, recent studies suggested that nonlinear measures can complement spectral analysis in order to understand brain dynamics in ADHD [ 15 ]. For instance, MEG data were analyzed by using Lempel-Ziv complexity [ 16 ] and fuzzy entropy [ 15 ]. They revealed that brain activity is less complex and more regular in ADHD patients compared to controls.…”

Section: Introductionmentioning

confidence: 99%

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MEG Analysis of Neural Interactions in Attention-Deficit/Hyperactivity Disorder

Khadmaoui

Gómez

Poza

et al. 2016

Computational Intelligence and Neuroscience

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The aim of the present study was to explore the interchannel relationships of resting-state brain activity in patients with attention-deficit/hyperactivity disorder (ADHD), one of the most common mental disorders that develop in children. Magnetoencephalographic (MEG) signals were recorded using a 148-channel whole-head magnetometer in 13 patients with ADHD (range: 8–12 years) and 14 control subjects (range: 8–13 years). Three complementary measures (coherence, phase-locking value, and Euclidean distance) were calculated in the conventional MEG frequency bands: delta, theta, alpha, beta, and gamma. Our results showed that the interactions among MEG channels are higher for ADHD patients than for control subjects in all frequency bands. Statistically significant differences were observed for short-distance values within right-anterior and central regions, especially at delta, beta, and gamma-frequency bands (p < 0.05; Mann-Whitney U test with false discovery rate correction). These frequency bands also showed statistically significant differences in long-distance interactions, mainly among anterior and central regions, as well as among anterior, central, and other areas. These differences might reflect alterations during brain development in children with ADHD. Our results support the role of frontal abnormalities in ADHD pathophysiology, which may reflect a delay in cortical maturation in the frontal cortex.

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“…Therefore, this is a feasible way to decompose the ship-radiated noise signal by IITD to extract effective ISCs.Entropy theory can efficiently evaluate the complexity of the time series and reduce the dimension of the feature vector and fully represent the characteristics of the series. Hence, there are many methods for complexity measurement, including Shannon entropy [26], sample entropy (SampEn) [27,28], permutation entropy (PE) [29], and fuzzy entropy [30], which have been successfully applied in the field of fault diagnosis and the medical field. However, SampEn is time consuming for large data calculations and is susceptible to mutated signals.…”

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confidence: 99%

A Novel Improved Feature Extraction Technique for Ship-Radiated Noise Based on IITD and MDE

Zhang

et al. 2019

Entropy

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Ship-radiated noise signal has a lot of nonlinear, non-Gaussian, and nonstationary information characteristics, which can reflect the important signs of ship performance. This paper proposes a novel feature extraction technique for ship-radiated noise based on improved intrinsic time-scale decomposition (IITD) and multiscale dispersion entropy (MDE). The proposed feature extraction technique is named IITD-MDE. First, IITD is applied to decompose the ship-radiated noise signal into a series of intrinsic scale components (ISCs). Then, we select the ISC with the main information through the correlation analysis, and calculate the MDE value as feature vectors. Finally, the feature vectors are input into the support vector machine (SVM) for ship classification. The experimental results indicate that the recognition rate of the proposed technique reaches 86% accuracy. Therefore, compared with the other feature extraction methods, the proposed method provides a new solution for classifying different types of ships effectively.Entropy 2019, 21, 1215 2 of 17 CEEMDAN and EE to extract the hybrid energy feature. Frei [17] proposed an adaptive time-frequency analysis method, which can decompose the nonstationary signal into a series of single component signals with the physical meanings of instantaneous frequencies. In recent decades, these studies have provided rich reference information, which is widely used in fault diagnosis [18,19], biomedicine [20,21], geophysics [22], and hydroacoustics [23,24]. Compared with the EMD method, the ITD method has obvious advantages in terms of computational efficiency and processing edge effects. However, the definition of the baseline of the ITD method is based on the linear transformation of the signal itself, and may cause a glitch and distortion of the proper rotation components obtained by the decomposition. Based on this, we used akima interpolation [25] to improve the ITD method; then, the IITD algorithm was proposed. Therefore, this is a feasible way to decompose the ship-radiated noise signal by IITD to extract effective ISCs.Entropy theory can efficiently evaluate the complexity of the time series and reduce the dimension of the feature vector and fully represent the characteristics of the series. Hence, there are many methods for complexity measurement, including Shannon entropy [26], sample entropy (SampEn) [27,28], permutation entropy (PE) [29], and fuzzy entropy [30], which have been successfully applied in the field of fault diagnosis and the medical field. However, SampEn is time consuming for large data calculations and is susceptible to mutated signals. While the PE is faster, it fails to consider the mean value of amplitudes and differences between the amplitudes value. In order to overcome the drawbacks of SampEn and PE, a new measure of the complexity method, named dispersion entropy (DE), was proposed by Mostafa Rostaghi and Hamed Azami in 2016 [31]. The advantage of the DE algorithm is that the calculation speed is fast, the influence of the noisy signal is small, ...

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MEG analysis of neural dynamics in attention-deficit/hyperactivity disorder with fuzzy entropy

Abstract: Abstract-

Cited by 26 publications

References 39 publications

Complexity analysis of fNIRS signals in ADHD children during working memory task

Complexity analysis of fNIRS signals in ADHD children during working memory task

MEG Analysis of Neural Interactions in Attention-Deficit/Hyperactivity Disorder

A Novel Improved Feature Extraction Technique for Ship-Radiated Noise Based on IITD and MDE

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