Sample entropy and surrogate data analysis for Alzheimer’s disease

Wang, Xue Wei; Zhao, Xiao Hu; Li, Fēi; Lin, Qiang; Hu, Zheng

doi:10.3934/mbe.2019345

Cited by 9 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 40 articles (Table 3 , Ref. [ 20 , 25 , 26 , 27 , 28 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]) published between 2000 and 2023 using entropy measures were included (Fig. 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…Studies that included AD patients matched with healthy controls (HC) [ 26 , 27 , 34 , 36 , 37 , 38 , 42 , 43 , 44 , 45 , 46 , 48 , 49 , 50 , 60 , 61 , 66 ] revealed lower entropy values, except for one study that used Spectral Entropy (SpectEn) [ 44 ]. This decrease in entropy levels in AD patients was observed in frontal [ 27 , 46 ], temporal [ 47 , 48 ], parietal [ 42 , 43 , 48 , 49 , 50 , 51 ], central [ 27 ], and occipital [ 28 , 42 , 43 , 49 , 50 ] areas. A recent study showed that the best differentiation between HC and probable AD was obtained using delta waves [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

Zúñiga,

Acero-González,

Restrepo-Castro

et al. 2024

Actas Esp Psiquiatr

View full text Add to dashboard Cite

Background: The number of individuals diagnosed with Alzheimer's disease (AD) has increased, and it is estimated to continue rising in the coming years. The diagnosis of this disease is challenging due to variations in onset and course, its diverse clinical manifestations, and the indications for measuring deposit biomarkers. Hence, there is a need to develop more precise and less invasive diagnostic tools. Multiple studies have considered using electroencephalography (EEG) entropy measures as an indicator of the onset and course of AD. Entropy is deemed suitable as a potential indicator based on the discovery that variations in its complexity can be associated with specific pathologies such as AD. Methodology: Following PRISMA guidelines, a literature search was conducted in 4 scientific databases, and 40 articles were analyzed after discarding and filtering. Results: There is a diversity in entropy measures; however, Sample Entropy (SampEn) and Multiscale Entropy (MSE) are the most widely used (21/40). In general, it is found that when comparing patients with controls, patients exhibit lower entropy (20/40) in various areas. Findings of correlation with the level of cognitive decline are less consistent, and with neuropsychiatric symptoms (2/40) or treatment response less explored (2/40), although most studies show lower entropy with greater severity. Machine learning-based studies show good discrimination capacity. Conclusions: There is significant difficulty in comparing multiple studies due to their heterogeneity; however, changes in Multiscale Entropy (MSE) scales or a decrease in entropy levels are considered useful for determining the presence of AD and measuring its severity.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

Zúñiga,

Acero-González,

Restrepo-Castro

et al. 2024

Actas Esp Psiquiatr

View full text Add to dashboard Cite

show abstract

“…Since then, it has had a widespread application in various domains of research, including neuroscience [37,38]. In another study [39] authors report the study of applying entropy to EEG data from patients and report that Alzheimer's disease could result in complexity loss. More relevant papers [40] related to this topic can also be found.…”

Section: Introductionmentioning

confidence: 99%

On the analysis of a heterogeneous coupled network of memristive Chialvo neurons.

Ghosh

Muni

Fatoyinbo

2023

Preprint

View full text Add to dashboard Cite

We perform a numerical study on the application of electromagnetic flux on a heterogeneous network of Chialvo neurons represented by a ring-star topology. Heterogeneities are realized by introducing additive noise modulations on both the central-peripheral and the peripheral-peripheral coupling links in the topology not only varying in space but also in time. The variation in time is understood by two coupling probabilities, one for the central-peripheral connections and the other for the peripheral-peripheral connections respectively, that update the network topology with each iteration in time. We have further reported the rich spatiotemporal patterns like two-cluster states, chimera states, coherent, and asynchronized states that arise throughout the network dynamics. We have also investigated the appearance of a special kind of asynchronization behavior called "solitary nodes" that have a wide range of applications pertaining to real-world nervous systems. In order to characterize the behavior of the nodes under the influence of these heterogeneities, we have studied two different metrics called the "cross-correlation coefficient" and the "synchronization error". Additionally, to capture the statistical property of the network, for example, how complex the system behaves, we have also studied a measure called "sample entropy". Various two-dimensional color-coded plots are presented in the study to exhibit how these metrics/measures behave with the variation of parameters.

show abstract

“…However, the analysis ended with the confirmation that the signals were nonlinear. In [40], the Sample Entropy (SampEn), a nonlinear feature, along with its SA counterpart was tested for EEG based Alzheimer's Disease (AD) detection. The SA score used was equivalent to the absolute value of the score considered in this paper.…”

Section: Introductionmentioning

confidence: 99%

Windowing Compensation in Fourier Based Surrogate Analysis and Application to EEG Signal Classification

Caza-Szoka

Massicotte

2022

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

This paper shows how adding a second step of windowing after each phase randomization can reduce the False Rejection Rate in Fourier based Surrogate Analysis. Windowing techniques reduce the discontinuities at the boundaries of the periodically extended data sequence in Fourier Series. However, they add a time domain non-stationarity which affects the Surrogate Analysis. This effect is particularly problematic for short low-pass signals. Applying the same window to the surrogate data allows having the same non-stationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte-Carlo simulations. Previous methods were not able to yield good performances for left-sided and rightsided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests as well as bilateral tests. In order to show that the proposed windowing method can be useful in real context, in this extended paper, it was applied for an EEG diagnostic problem. A dataset comprising the EEG measurements of 15 subject distributed in three groups: attention-deficit disorder primarily hyperactiveimpulsive (ADHD), attention-deficit disorder primarily inattentive (ADD); and anxiety with attentional fragility (ANX) was used. Both statistical and machine learning (Naïve Bayesian) approaches were considered. The Mean Short Windowed SA (MSWSA) was used as a signal feature and its performances was studied with respect to the windowing systems. The main findings were that (i) the MSWSA feature has less variability for ADD than for ADHD or ANX, (ii) the proposed windowing method reduces bias and non-normality of the SA feature, (iii) with the proposed method and a naïve Bayesian classifier, a 93% success rate of discriminating ADD from ADHD and ANX was achieved with leave-one-out crossvalidation, and (iv) the new feature could not have yielded interesting results without the proposed windowing system.

show abstract

Sample entropy and surrogate data analysis for Alzheimer’s disease

Cited by 9 publications

References 37 publications

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

On the analysis of a heterogeneous coupled network of memristive Chialvo neurons.

Windowing Compensation in Fourier Based Surrogate Analysis and Application to EEG Signal Classification

Contact Info

Product

Resources

About