Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

Abásolo, Daniel; Escudero, Javier; Hornero, Roberto; Gómez, Carlos; Espino, Pedro

doi:10.1007/s11517-008-0392-1

Cited by 127 publications

(109 citation statements)

References 48 publications

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“…Our results support the idea that the AMIFRD, which is correlated with signal entropy (Abásolo et al 2008, Paluš 1996, is an irregularity estimator instead of a complexity measure, as some authors have previously suggested (Jeong et al 2001, Na et al 2002, Palacios et al 2007. From a strict point of view, a quantitative measure of complexity should vanish for both completely ordered and completely random signals like white noise, which is very unpredictable but not structurally complex (Costa et al 2005).…”

Section: Resultssupporting

confidence: 87%

“…Furthermore, the use of the AMIFRD as a characterising metric in the analysis of experimental recordings has some advantages. Firstly, it can be applied to short time series in comparison to other non-linear classical analysis methods, such as the correlation dimension (Abásolo et al 2008, Gómez et al 2007, Jeong et al 2001. Secondly, the only input parameter for the AMIF is the number of histogram partitions (Jeong et al 2001, Na et al 2002.…”

Section: Resultsmentioning

confidence: 99%

“…It is based on the amplitude distributions of ( ) t X on different time scales, τ (David et al 2004). These amplitude distributions are estimated from histograms (Abásolo et al 2008, Alonso et al 2007, Gómez et al 2007, Jeong et al 2001.…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

“…in a least-squares sense (Abásolo et al 2008, Gómez et al 2007, Jeong et al 2001. Therefore, different time scales were simultaneously considered when analysing the signal.…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

“…This methodology can be useful when no information is known in advance about the prominent time scales of a signal, as it often occurs in biomedical applications (Hoyer et al 2005, Palacios et al 2007. This rate of decrease measures the information loss versus τ (Hoyer et al 2002) and it is correlated with signal entropy (Abásolo et al 2008, Paluš 1996.…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

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Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

2009

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The mutual information (MI) is a measure of both linear and non-linear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterise biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the non-linear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

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Section: Resultssupporting

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

“…in a least-squares sense (Abásolo et al 2008, Gómez et al 2007, Jeong et al 2001. Therefore, different time scales were simultaneously considered when analysing the signal.…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

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Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

2009

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Current awareness in geriatric psychiatry

2009

Int J Geriat Psychiatry

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In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of geriatric psychiatry. Each bibliography is divided into 9 sections: 1 Reviews; 2 General; 3 Assessment; 4 Epidemiology; 5 Therapy; 6 Care; 7 Dementia; 8 Depression; 9 Psychology. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted

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Neural alignment during face‐to‐face spontaneous deception: Does gender make a difference?

Chen

Zhang

et al. 2020

Human Brain Mapping

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This study investigated the gender differences in deception and their neural basis in the perspective of two‐person neuroscience. Both male and female dyads were asked to perform a face‐to‐face spontaneous sender–receiver deception task, while their neural activities in the prefrontal cortex (PFC) and right temporal parietal junction (rTPJ) were recorded simultaneously using functional near‐infrared spectroscopy (fNIRS)‐based hyperscanning. Male and female dyads displayed similar deception rate, successful deception rate, and eye contact in deception trials. Moreover, eye contact in deception trials was positively correlated with the success rate of deception in both genders. The fNIRS data showed that the interpersonal neural synchronization (INS) in PFC was significantly enhanced only in female dyads when performed the deception task, while INS in rTPJ was increased only in male dyads. Such INS was correlated with the success rate of deception in both dyads. Granger causality analysis showed that no significant directionality between time series of PFC (or rTPJ) in each dyad, which could indicate that sender and receiver played equally important role during deception task. Finally, enhanced INS in PFC in female dyads mediated the contribution of eye contact to the success rate of deception. All findings in this study suggest that differential patterns of INS are recruited when male and female dyads perform the face‐to‐face deception task. To our knowledge, this is the first interbrain evidence for gender difference of successful deception, which could make us a deeper understanding of spontaneous face‐to‐face deception.

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Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

Cited by 127 publications

References 48 publications

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

Current awareness in geriatric psychiatry

Neural alignment during face‐to‐face spontaneous deception: Does gender make a difference?

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